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Marchese, Emiliano. "Optimizing complex networks models." Thesis, IMT Alti Studi Lucca, 2022. http://e-theses.imtlucca.it/356/1/Marchese_phdthesis.pdf.
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Analyzing real-world networks ultimately amounts at com-
paring their empirical properties with the outcome of a proper, statistical model. The far most common, and most useful, approach to define benchmarks rests upon the so-called canonical formalism of statistical mechanics which has led to the definition of the broad class of models known as Exponential Random Graphs (ERGs). Generally speaking, employing a model of this family boils down at maximizing a likelihood function that embodies the available information about a certain system, hence constituting the desired benchmark. Although powerful, the aforementioned models cannot be solved analytically, whence the need to rest upon numerical recipes for their optimization. Generally speaking, this is a hard task, since real-world networks can be enormous in size (for example, consisting of billions of nodes and links), hence requiring models with ‘many’ parameters (say, of the same order of magnitude of the number of nodes). This evidence calls for optimization algorithms which are both fast and scalable: the collection of works constituting the present thesis represents an attempt to fill this gap. Chapter 1 provides a
quick introduction to the topic. Chapter 2 deals specifically with ERGs: after reviewing the basic concepts constituting the pillars upon which such a framework is based, we will discuss several instances of it and three different numerical techniques for their optimization. Chapter 3, instead, focuses on the detection of mesoscale structures and, in particular, on the formalism based upon surprise: as the latter allows any partition of nodes to be assigned a p-value, detecting a specific, mesoscale structural organization can be understood as the problem of finding the corresponding, most significant partition - i.e. an optimization problem whose score function is, precisely, surprise. Finally, chapter 4 deals with the application of a couple of ERGs and of the surprise-based formalism to cryptocurrencies (specifically, Bitcoin).
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Unicomb, Samuel Lee. "Threshold driven contagion on complex networks." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEN003.
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Les interactions entre les composants des systèmes complexes font émerger différents types de réseaux. Ces réseaux peuvent jouer le rôle d’un substrat pour des processus dynamiques tels que la diffusion d’informations ou de maladies dans des populations. Les structures de ces réseaux déterminent l’évolution d’un processus dynamique, en particulier son régime transitoire, mais aussi les caractéristiques du régime permanent. Les systèmes complexes réels manifestent des interactions hétérogènes en type et en intensité. Ces systèmes sont représentés comme des réseaux pondérés à plusieurs couches. Dans cette thèse, nous développons une équation maîtresse afin d’intégrer ces hétérogénéités et d’étudier leurs effets sur les processus de diffusion. À l’aide de simulations mettant en jeu des réseaux réels et générés, nous montrons que les dynamiques de diffusion sont liées de manière non triviale à l’hétérogénéité de ces réseaux, en particulier la vitesse de propagation d’une contagion basée sur un effet de seuil. De plus, nous montrons que certaines classes de réseaux sont soumises à des transitions de phase réentrantes fonctions de la taille des “global cascades”. La tendance des réseaux réels à évoluer dans le temps rend difficile la modélisation des processus de diffusion. Nous montrons enfin que la durée de diffusion d’un processus de contagion basé sur un effet de seuil change de manière non-monotone du fait de la présence de “rafales” dans les motifs d’interactions. L’ensemble de ces résultats mettent en lumière les effets de l’hétérogénéité des réseaux vis-à-vis des processus dynamiques y évoluant<br>Networks arise frequently in the study of complex systems, since interactions among the components of such systems are critical. Net- works can act as a substrate for dynamical process, such as the diffusion of information or disease throughout populations. Network structure can determine the temporal evolution of a dynamical process, including the characteristics of the steady state. The simplest representation of a complex system is an undirected, unweighted, single layer graph. In contrast, real systems exhibit heterogeneity of interaction strength and type. Such systems are frequently represented as weighted multiplex networks, and in this work we in- corporate these heterogeneities into a master equation formalism in order to study their effects on spreading processes. We also carry out simulations on synthetic and empirical networks, and show that spread- ing dynamics, in particular the speed at which contagion spreads via threshold mechanisms, depend non-trivially on these heterogeneities. Further, we show that an important family of networks undergo reentrant phase transitions in the size and frequency of global cascades as a result of these interactions. A challenging feature of real systems is their tendency to evolve over time, since the changing structure of the underlying network is critical to the behaviour of overlying dynamical processes. We show that one aspect of temporality, the observed “burstiness” in interaction patterns, leads to non-monotic changes in the spreading time of threshold driven contagion processes. The above results shed light on the effects of various network heterogeneities, with respect to dynamical processes that evolve on these networks
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Roth, Camille. "Co-evolution in epistemic networks : reconstructing social complex systems." Palaiseau, Ecole polytechnique, 2005. http://www.theses.fr/2005EPXX0057.
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Des agents produisant, manipulant et échangeant des connaissances constituent un système complexe socio-sémantique, dont l’étude représente un défi à la fois théorique, dans la perspective d’étendre la naturalisation des sciences sociales, et pratique, avec des applications permettant aux agents de connaître la dynamique du système dans lequel ils évoluent. Cette thèse se situe dans le cadre de ce programme de recherche. Parallèlement et plus largement, nous nous intéressons à la question de la reconstruction en sciences sociales. La reconstruction est un problème inverse comprenant deux volets complémentaires : (i) la déduction d’observations de haut-niveau à partir de phénomènes de bas-niveau ; et (ii) la reproduction de l’évolution des observations de haut-niveau à partir de la dynamique des objets de bas-niveau. Nous affirmons que plusieurs aspects significatifs de la structure d’une communauté de savoirs sont principalement produits par la dynamique d’un réseau épistémique où co-évoluent agents et concepts. En particulier, nous résolvons le premier volet du problème de la reconstruction en utilisant des treillis de Galois afin de recréer des taxonomies de communautés de savoirs à partir de simples relations entre agents et concepts; nous obtenons de fait une description historique se rapportant à la progression des champs, leur déclin, leur spécialisation ou leurs interactions (fusion ou scission). Nous micro-fondons ensuite la structure de ces communautés de savoirs en exhibant et en estimant empiriquement des processus d’interaction au niveau des agents, en co-évolution avec les concepts au sein du réseau épistémique, qui rendent compte de la morphogenèse et de l’émergence de plusieurs faits stylisés structurels de haut-niveau—il s’agit là du deuxième volet. Nous défendons finalement un point de vue épistémologique concernant la méthodologique générale de reconstruction d’un système complexe qui appuie notre choix d’un cadre coévolutionnaire<br>Agents producing and exchanging knowledge are forming as a whole a socio-semantic complex system. Studying such knowledge communities offers theoretical challenges, with the perspective of naturalizing further social sciences, as well as practical challenges, with potential applications enabling agents to know the dynamics of the system they are participating in. The present thesis lies within the framework of this research program. Alongside and more broadly, we address the question of reconstruction in social science. Reconstruction is a reverse problem consisting of two issues: (i) deduce a given high-level observation for a considered system from low-level phenomena; and (ii) reconstruct the evolution of high-level observations from the dynamics of lower-level objects. In this respect, we argue that several significant aspects of the structure of a knowledge community are primarily produced by the co-evolution between agents and concepts, i. E. The evolution of an epistemic network. In particular, we address the first reconstruction issue by using Galois lattices to rebuild taxonomies of knowledge communities from low-level observation of relationships between agents and concepts; achieving ultimately an historical description (inter alia field progress, decline, specialization, interaction - merging or splitting). We then micro-found various stylized facts regarding this particular structure, by exhibiting processes at the level of agents accounting for the emergence of epistemic community structure. After assessing the empirical interaction and growth processes, and assuming that agents and concepts are co-evolving, we successfully propose a morphogenesis model rebuilding relevant high-level stylized facts. We finally defend a general epistemological point related to the methodology of complex system reconstruction, eventually supporting our choice of a co-evolutionary framework
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Savoy, Daniel Prata. "A dinâmica de opinião dos debates públicos em redes sociais complexas." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/100/100132/tde-04022013-114700/.
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Neste trabalho são estudados os efeitos, causados por variações na topologia de rede, no comportamento de quatro modelos de dinâmica de opinião: o Modelo Votante, o Modelo Confiança Limitada, o Modelo da Regra da Maioria e o Modelo CODA. Primeiramente estes modelos são utilizados em simulações que usam uma série de diversas redes sociais complexas, geradas para apresentar diferentes combinações de valores de certas propriedades chave, como aglomeração, conectividade, assortatividade e distâncias internas. Em seguida, são realizados experimentos que mostram como a topologia influencia os resultados na modelagem de cenários de debates públicos, onde duas opiniões rivais, A e B, disputam sob condições desiguais o consenso de uma população simulada.<br>This work studies the effects caused by variations in network topology in the behavior of four different models of opinion dynamics: the Voter Model, Bounded Confidence Model, the Majority Rule Model and the CODA Model. First, these models are used in simulations over a number of different complex social networks, generated to show sereval combinations of key properties such as clustering, connectivity, assortativity and path distances. Then, we perform experiments that show how the topology influences the results in modeling scenarios of public debates, where two rival opinions, A and B, compete under unequal conditions for the consensus of a simulated population.
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Ciotti, Valerio. "Positive and negative connections and homophily in complex networks." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/31787.
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In this thesis I investigate the effects of positive and negative connections on social and organization networks, and the presence and role of homophily in networks of scientific collaborations and citations through the combination of methodologies borrowed from complexity science, statistics, and organizational sciences. In the first part of the thesis, I study the differences between patterns of positive and negative connections among individuals in two online signed social networks. Findings suggest that the sign of links in a social network shapes differently the network's topology: there is a positive correlation between the degrees of two nodes, when they share a positive connection, and a negative correlation when they share a negative connection. I then move my focus to the study of a dataset on start-ups from which I construct and analyse the competition and mobility networks among companies. Results show that the presence of competition has negative effects on the mobility of people among companies and on the success of the start-up ecosystem of a nation. Competitive behaviours may also emerge in science. Therefore, in the second part of this thesis, I focus on a database of all papers and authors who have published in the American Physical Society (APS) journals. Through the analysis of the citation network of the APS, I propose a method that aims to statistically validate the presence (or absence) of a citation between any two articles. Results show that homophily is an important mechanism behind the citation between articles: the more two articles share similar bibliographies, i.e., deal with similar arguments, the more likely there is a citation between them. In the last chapter, I investigate the presence of homophily in the APS data set, this time at the level of the collaboration network among sci- entists. Results show that homophily can be responsible in fostering collaboration, but above a given point the effect of similarity decreases the probability of a collaboration. Additionally, I propose a model that successfully reproduces the empirical findings.
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Abreu, Luís Fernando Dorelli de. "Estrutura e dinâmica de redes de informação." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-08112016-091004/.
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O aumento na disponibilidade de dados referentes a interação entre pessoas online tornou possível o estudo o processo de propagação de informações em redes sociais com volumes de dado antes jamais pensados. Neste trabalho, utilizamos dados do site de micro-blogging Twitter juntamente com conceitos de redes complexas para entender, caracterizar e classificar processos de difusão de informação observados nessa plataforma e em redes sociais em geral. Apresentamos importantes medidas para caracterização de cascatas de informação, bem como algoritmos eficientes para o seu cálculo. Com o auxilio dessas, mostramos que é possível quantificar a influência da rede social no processo de propagação de informação. Em seguida, constatamos que a informação tende a propagar por caminhos mínimos nessa rede. Por fim, mostramos que é possível utilizar apenas a topologia da rede social, sem nenhuma informação semântica, para agrupar tópicos, e que a topologia da rede social é fortemente influenciada pelos assuntos falados nela. Apesar de nosso trabalho possuir como base um único dataset, os métodos e medidas desenvolvidos são gerais e podem ser aplicados a qualquer processo de difusão de informação e a qualquer rede complexa.<br>The raise in the availability of data regarding interactions between people online has opened new doors to study the process of information diffusion in social networks. In this present work, we make use of the data from the micro-blogging website Twitteralong with complex networks concepts to understand, characterize and classify information diffusion processes observed in this platform and in social networks in general. We present important measures to characterize information cascades and efficient algorithms to calculate them. With the help of these measures, we show that it is possible to quantify the influence of the social network in the process of information diffusion. After that, we show that information does tend to travel along shortest paths on Twitter. Finally, we show that the topology of the social network, without any extra semantic information, can be used to aggregate topics, and that such topology is highly influenced by the topics being discussed on it. Altough we work with only a single dataset, our methods and measures developed are general and can be applied to any process of information diffusion and any complex network.
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Libardi, Paula Luciene Oliveira 1980. "Detecção computacional de falecidos em redes sociais online." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/267725.
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Orientadores: André Franceschi de Angelis, Regina Lúcia de Oliveira Moraes<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Tecnologia<br>Made available in DSpace on 2018-08-27T04:53:50Z (GMT). No. of bitstreams: 1
Libardi_PaulaLucieneOliveira_M.pdf: 1610224 bytes, checksum: a08b75cd1a30c421927617ee8b6ac8d4 (MD5)
Previous issue date: 2015<br>Resumo: A identificação de usuários falecidos em Redes Sociais Online é um desafio em aberto e, dado o tamanho das principais redes, abordagens que envolvam intervenção manual são impraticáveis. Usuários inativos por longo tempo inviabilizam soluções simples tais como a expiração de um prazo desde o último acesso, o que torna difícil a diferenciação entre inativos e falecidos. Esta pesquisa iniciou-se com o pressuposto de que o problema poderia ser parcialmente resolvido com métodos automáticos e a hipótese era de que dois métodos aqui propostos, um baseado na análise de frequência de mensagens trocadas entre usuários e outro fundamentado na combinação de informações da topologia da rede junto a inspeções de mensagens, poderiam identificar satisfatoriamente parte dos usuários falecidos. Para testar esta hipótese, recorreu-se à simulação computacional, usando topologias livre de escala e aleatória. O programa que simula as redes foi construído de forma a aplicar e testar os métodos de identificação de falecidos, seguindo padrões de projeto que permitem facilmente a troca ou o encadeamento dos algoritmos a validar. Dessa característica, originou-se um terceiro método, que é a combinação das saídas de algoritmos detectores aplicados anteriormente à rede. Os resultados da pesquisa validaram a hipótese, sendo que os dois métodos propostos inicialmente tiveram, cada qual, índices de acerto superiores a 70% na maioria dos casos simulados, independentemente da topologia da rede. Em ambos os métodos, no entanto, é necessária uma calibração de dois parâmetros operacionais, o que exige algum conhecimento da rede examinada e influencia na taxa de detecção. O último método mostrou-se bastante eficiente, com detecção correta superior a 94%, e capaz de absorver flutuações na taxa de detecção dos demais métodos advindas de suas respectivas parametrizações. Portanto, os objetivos da pesquisa foram plenamente atingidos, com a validação da hipótese inicial, a proposta de três métodos para a solução do problema e a geração de um produto tecnológico, o Demortuos, que é o software de simulação da rede e teste dos métodos, atualmente em processo de registro no Instituto Nacional da Propriedade Industrial (INPI). Adicionalmente, foram abertas possibilidades para o desenvolvimento de métodos automáticos para busca de outras classes de usuários<br>Abstract: Identifying deceased users in Online Social Networks is an open challenge and, given the size of the main networks, approaches involving manual intervention are impractical. Inactive users for a long time prevent simple solutions such as the expiration of a period since the last entry, making it difficult to differentiate between inactive and deceased users. This research began with the assumption that the problem could be partially solved with automated methods and the hypothesis was that two methods proposed here, one based on frequency analysis of messages exchanged between users and the other based on the combination of topology information network with the messages of inspections, could satisfactorily identify the part of deceased users. To test this hypothesis, we used the computer simulation, using free topologies of scale and random, the latter for comparison purposes. The program that simulates the network was constructed to implement and test the deceased identification methods, following design patterns that easily allow the exchange or the chain of algorithms to validate. This characteristic gave up a third method, which is combining the outputs of detectors algorithms previously applied to the network. The survey results validated the hypothesis, and the two proposed methods initially had, each, hit rates of over 70% in most cases simulated, regardless of the network topology. In both methods, however, two operating parameters calibration is necessary, which requires some knowledge of the network and examined influences the detection rate. The last method proved to be very efficient with proper detection above 94%, and able to absorb fluctuations in the detection rate of other methods resulting from their respective parameterization. Therefore, the research objectives were fully achieved, with the validation of the initial hypothesis, the proposed three methods for the solution of the problem and the generation of a technological product, Demortuos, which is the network simulation software and testing methods currently in the registration process at the National Institute of Industrial Property (INPI). Moreover, possibilities are opened for the development of automated methods to search for other classes of users<br>Mestrado<br>Tecnologia e Inovação<br>Mestra em Tecnologia
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Grabowicz, Przemyslaw Adam. "Complex networks approach to modeling online social systems. The emergence of computational social science." Doctoral thesis, Universitat de les Illes Balears, 2014. http://hdl.handle.net/10803/131220.
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This thesis is devoted to quantitative description, analysis, and modeling of complex social systems in the form of online social networks. Statistical patterns of the systems under study are unveiled and interpreted using concepts and methods of network science, social network analysis, and data mining. A long-term promise of this research is that predicting the behavior of complex techno-social systems will be possible in a way similar to contemporary weather forecasting, using statistical inference and computational modeling based on the advancements in understanding and knowledge of techno-social systems. Although the subject of this study are humans, as opposed to atoms or molecules in statistical physics, the availability of extremely large datasets on human behavior permits the use of tools and techniques of statistical physics. This dissertation deals with large datasets from online social networks, measures statistical patterns of social behavior, and develops quantitative methods, models, and metrics for complex techno-social systems.<br>La presente tesis está dedicada a la descripción, análisis y modelado cuantitativo de sistemas complejos sociales en forma de redes sociales en internet. Mediante el uso de métodos y conceptos provenientes de ciencia de redes, análisis de redes sociales y minería de datos se descubren diferentes patrones estadísticos de los sistemas estudiados. Uno de los objetivos a largo plazo de esta línea de investigación consiste en hacer posible la predicción del comportamiento de sistemas complejos tecnológico-sociales, de un modo similar a la predicción meteorológica, usando inferencia estadística y modelado computacional basado en avances en el conocimiento de los sistemas tecnológico-sociales. A pesar de que el objeto del presente estudio son seres humanos, en lugar de los átomos o moléculas estudiados tradicionalmente en la física estadística, la disponibilidad de grandes bases de datos sobre comportamiento humano hace posible el uso de técnicas y métodos de física estadística. En el presente trabajo se utilizan grandes bases de datos provenientes de redes sociales en internet, se miden patrones estadísticos de comportamiento social, y se desarrollan métodos cuantitativos, modelos y métricas para el estudio de sistemas complejos tecnológico-sociales.
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Grando, Felipe. "On the analysis of centrality measures for complex and social networks." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/122516.
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Recentemente, as medidas de centralidade ganharam relevância nas pesquisas com redes complexas e redes sociais, atuando como preditores comportamentais, na identificação de elementos de poder e influência, na detecção de pontos estratégicos para a comunicação e para a transmissão de doenças. Novas métricas foram criadas e outras reformuladas, mas pouco tem sido feito para que se entenda a relação existente entre as diferentes medidas de centralidades, assim como sua relação com outras propriedades estruturais das redes em que elas são frequentemente aplicadas. Nossa pesquisa visa analisar e estudar essas relações para que sirvam de guia na aplicação das medidas de centralidade existentes em novos contextos e aplicações. Nós apresentamos também evidencias que indicam um desempenho superior das medidas conhecidas como Walk Betweenness, Information, Eigenvector and Betweenness na distinção de vértices das redes somente pelas suas características estruturais. Ainda, nós propiciamos detalhes sobre o desempenho distinto de cada métrica de acordo com o tipo de rede em que se trabalha. Adicionalmente, mostramos que várias das medidas de centralidade apresentam um alto nível de redundância e concordância entre si (com correlação superior a 0,8). Um forte indício que o uso simultâneo de várias métricas é improdutivo ou pouco eficaz. Os resultados da nossa pesquisa reforçam a ideia de que para usar apropriadamente as medidades de centralidade é de extrema importância que se saiba mais sobre o comportamento e propriedades das mesmas, fato que salientamos nessa dissertação.<br>Over the last years, centrality measures have gained importance within complex and social networks research, e.g., as predictors of behavior, identification of powerful and influential elements, detection of critical spots in communication networks and in transmission of diseases. New measures have been created and old ones reinvented, but few have been proposed to understand the relation among measures as well as between measures and other structural properties of the networks. Our research analyzes and studies these relations with the objective of providing a guide to the application of existing centrality measures for new environments and new purposes. We shall also present evidence that the measures known as Walk Betweenness, Information, Eigenvector and Betweenness are substantially better than other metrics in distinguishing vertices in a network by their structural properties. Furthermore, we provide evidence that each metric performs better with respect to distinct kinds of networks. In addition, we show that most metrics present a high level of redundancy (over 0.8 correlation) and its simultaneous use, in most cases, is fruitless. The results achieved in our research reinforce the idea that to use centrality measures properly, knowledge about their underlying properties and behavior is valuable, as we show in this dissertation.
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Amiri, Babak. "Evolutionary Algorithms for Community Detection in Complex Networks." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/10451.
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In recent years there has been a surge of community detection study of complex network analysis, since communities often play important roles in network systems. Most contemporary community detection algorithms employ single optimization criteria (i.e., modularity), which may not be adequate to represent the structures in complex networks. We suggest a community detection process as a Multi-Objective Optimization Problem (MOP) for investigating the community structures in complex networks. To overcome the limitations of community detection problems, we propose new multi-objective optimization algorithms: a Modified Harmony Search Algorithm, a Hybrid Chaotic Local Search-Harmony Search Algorithm (CLS-HAS) and an Enhanced Firefly Algorithm (EFF). A new tuning parameter based on a chaotic mechanism and novel self-adaptive probabilistic mutation strategies is used to improve the overall performance of the EFF algorithm. Although much of the focus of community detection techniques has been on identifying disjoint and static communities, almost all real networks are dynamic in nature. Detecting communities in dynamic networks is very challenging and the analysis of dynamic communities is still considered to be in its infancy. To study the structure of communities in dynamic networks, we consider an evolution-based clustering method with the aim of maximizing cluster accuracy and minimizing clustering drift from one time step to the next. In this study, the detection of communities with temporal smoothness is formulated as a multi-objective problem and the Modified Bee Swarm Optimization (MBSO) is proposed to solve the community detection problem. The MBSO algorithm uses three kinds of bees, which have different moving pattern, to explore the entire search space and prevent premature convergence. The proposed algorithm has several remarkable characteristics to enhance the search capability of the original bee swarm optimization (BSO) for finding Pareto optimal solutions. Many real networks have complex overlapping community structures. This research also proposes a novel Fungi Optimization Algorithm (FOA) to discover overlapping communities. Unlike conventional algorithms based on node clustering, the proposed algorithm is based on link clustering.
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Grönlund, Andreas. "Complex patterns : from physical to social interactions." Doctoral thesis, Umeå universitet, Institutionen för fysik, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-801.
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Interactions are what gives us the knowledge of the world around us. Interactions on all levels may fundamentally be seen as an exchange of information and a possible response of the same. Whether it is an electron in an electrical field or a handsome dude in a bar responding to a flirtation---interactions make things happen. In this sense we can see that objects without the capability of interacting with each other also are invisible to each other. Chains of pairwise interacting entities can serve as mediators of indirect interactions between objects. Nonetheless, in the limit of no interactions, we get into a philosophical debate whether we actually may consider anything to exist since it can not be detected in any way. Interactions between matter tend to be organized and show a hierarchical structure in which smaller sub-systems can be seen as parts of a bigger system, which in turn might be a smaller part of an even bigger system. This is reflected by the fact that we have sciences that successfully study specific interactions between objects or matter---physics, chemistry, biology, ecology, sociology,... What happens in a situation where all length scales are important? How does the structure of the underlying network of interactions affect the dynamical properties of a system? What network structures do we find and how are they created? This thesis is a physicist's view of collective dynamics, from superconductors to social systems and navigation in city street networks.
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Nastos, James. "Utilizing graph classes for community detection in social and complex networks." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/53014.
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Social network analysis is a cross-disciplinary study of interest to mathematicians, physicists, computer scientists and sociologists. It deals with looking at large networks of interactions and extracting useful or meaningful information from them. One attribute of interest is that of identifying social communities within a network: how such a substructure should be defined is a widely-studied problem in itself. With each new definition, there is a need to study in what applications or context such a definition is appropriate, and develop algorithms and complexity results for the computation of these clusterings.
This thesis studies problems related to graph clustering, motivated by the social networking problem of community detection. One main contribution of this thesis is a new definition of a specific kind of family-like community, accompanied by theoretical and computational justifications. Additional results in this thesis include proofs of hardness for the quasi-threshold editing problem and the diameter augmentation problem, as well as improved exact algorithms for cograph and quasi-threshold edge deletion and vertex deletion problems.<br>Graduate Studies, College of (Okanagan)<br>Graduate
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Hill, Robert M. Martin Barbara N. "Leadership capacity in a complex connected age." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/7033.
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Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 26, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. Barbara N. Martin. Vita. Includes bibliographical references.
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Hannesson, Kristófer. "Identifying Important Members in a Complex Online Social Network." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-216947.
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The success of Online Social Networks (OSN) is influenced by having the ability to understand who is important. An OSN can be viewed as a graph where users are vertices and their interactions are edges. Graph-based methods can enable identification of people in these networks who for example exhibit the characteristics of leaders, influencers, or information brokers. A Massively Multiplayer Online game (MMO) is a type of OSN. It is a video game where a large number of players interact with each other in a virtual world. Using behavioral data of players' interactions within the space-based MMO EVE Online, the aim of this thesis is to conduct an experimental study to evaluate the effectiveness of a number graph-based methods at finding important players within different behavioral contexts. For that purpose we extract behavioral data to construct four distinct graphs: Fleet, Aggression, Mail, and Market. We also create a ground truth data set of important players based on heuristics from key gameplay categories. We experiment on these graphs with a selection of graph centrality, Influence Maximization, and heuristic methods. We explore how they perform in terms of ground truth players found per graph and execution time, and when combining results from all graphs. Our results indicate that there is no optimal method across graphs but rather the method and graph should be chosen according to the business intention at each time. To that end we provide recommendations as well as potential business case usages. We believe that this study serves as a starting point towards more graph based analysis within the EVE Online virtual universe where there are many unexplored research opportunities.<br>Framgången hos Online Sociala Nätverk (OSN) påverkas av förmågan att förstå vem som är viktig. Ett OSN kan ses som en graf där användarna är noder och deras interaktioner ärbågar. Grafbaserade metoder kan möjliggöra identifiering av personer i dessa nätverk somtill exempel uppvisar egenskaper hos ledare, påverkare eller informationsförmedlare. Ett Massively Multiplayer Online game (MMO) representerar en typ av OSN. Det är ett datorrollspel där ett stort antal spelare interagerar med varandra i en virtuell värld. Genomatt använda beteendedata om spelarnas interaktioner i den rymdbaserade MMO:n EVE Online är målet med denna avhandling att genomföra en experimentell studie för att utvärdera effektiviteten hos ett antal grafbaserade metoder för att hitta viktiga spelare inom olika beteendemässiga sammanhang. För det ändamålet extraherar vi beteendedata för att konstruera fyra distinkta grafer: Fleet, Aggression, Mail och Market. Vi skapar också ett ground truth" dataset av viktiga spelare baserat på heuristik från viktiga spelkategorier. Vi utför experiment på dessa grafer med ett urval av grafcentralitet, Influence Maximization och heuristiska metoder. Vi undersöker hur metoderna presterar i termer av antal ground truth spelare som finns per graf och över grafer, och i termer av exekveringstid. Våra resultat tyder på att det inte finns någon optimal metod för alla grafer. Metoden och grafen bör väljas beroende på intentionen vid varje tillfälle. För detta ändamål tillhandahåller vi rekommendationer samt potentiella affärsmässiga användningsområden. Vi tror att denna studie tjänar som utgångspunkt för mer grafbaserad analys inom EVEOnlines virtuella universum där det finns många outforskade forskningsmöjligheter.
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Bell, Patrick M. "Development of Local Homeland Security Networks in the State of Florida: A Social Network Analysis Approach." FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/574.
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How do local homeland security organizations respond to catastrophic events such as hurricanes and acts of terrorism? Among the most important aspects of this response are these organizations ability to adapt to the uncertain nature of these “focusing events” (Birkland 1997). They are often behind the curve, seeing response as a linear process, when in fact it is a complex, multifaceted process that requires understanding the interactions between the fiscal pressures facing local governments, the institutional pressures of working within a new regulatory framework and the political pressures of bringing together different levels of government with different perspectives and agendas.
This dissertation has focused on tracing the factors affecting the individuals and institutions planning, preparing, responding and recovering from natural and man-made disasters. Using social network analysis, my study analyzes the interactions between the individuals and institutions that respond to these “focusing events.” In practice, it is the combination of budgetary, institutional, and political pressures or constraints interacting with each other which resembles a Complex Adaptive System (CAS).
To investigate this system, my study evaluates the evolution of two separate sets of organizations composed of first responders (Fire Chiefs, Emergency Management Coordinators) and community volunteers organized in the state of Florida over the last fifteen years. Using a social network analysis approach, my dissertation analyzes the interactions between Citizen Corps Councils (CCCs) and Community Emergency Response Teams (CERTs) in the state of Florida from 1996- 2011. It is the pattern of interconnections that occur over time that are the focus of this study.
The social network analysis revealed an increase in the amount and density of connections between these organizations over the last fifteen years. The analysis also exposed the underlying patterns in these connections; that as the networks became more complex they also became more decentralized though not in any uniform manner. The present study brings to light a story of how communities have adapted to the ever changing circumstances that are sine qua non of natural and man-made disasters
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Gabardo, Ademir cristiano. "A heuristic to detect community structures in dynamic complex networks." Universidade Tecnológica Federal do Paraná, 2014. http://repositorio.utfpr.edu.br/jspui/handle/1/970.
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Complex networks are ubiquitous; billions of people are connected through social networks; there is an equally large number of telecommunication users and devices generating implicit complex networks. Furthermore, several structures can be represented as complex networks in nature, genetic data, social behavior, financial transactions and many other structures. Most of these complex networks present communities in their structure. Unveiling these communities is highly relevant in many fields of study. However, depending on several factors, the discover of these communities can be computationally intensive. Several algorithms for detecting communities in complex networks have been introduced over time. We will approach some of them. Our goal in this work is to identify or create an understandable and applicable heuristic to detect communities in complex networks, with a focus on time repetitions and strength measures. This work proposes a semi-supervised clustering approach as a modification of the traditional K-means algorithm submitting each dimension of data to a weight in order to obtain a weighted clustering method. As a first case study, databases of companies that have participated in public bids in Paraná state, will be analyzed to detect communities that can suggest structures such as cartels. As a second case study, the same methodology will be used to analyze datasets of microarray data for gene expressions, representing the correlation of the genes through a complex network, applying community detection algorithms in order to witness such correlations between genes.
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Erlandsson, Fredrik. "Human Interactions on Online Social Media : Collecting and Analyzing Social Interaction Networks." Doctoral thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15503.
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Online social media, such as Facebook, Twitter, and LinkedIn, provides users with services that enable them to interact both globally and instantly. The nature of social media interactions follows a constantly growing pattern that requires selection mechanisms to find and analyze interesting data. These interactions on social media can then be modeled into interaction networks, which enable network-based and graph-based methods to model and understand users’ behaviors on social media. These methods could also benefit the field of complex networks in terms of finding initial seeds in the information cascade model. This thesis aims to investigate how to efficiently collect user-generated content and interactions from online social media sites. A novel method for data collection that is using an exploratory research, which includes prototyping, is presented, as part of the research results in this thesis. Analysis of social data requires data that covers all the interactions in a given domain, which has shown to be difficult to handle in previous work. An additional contribution from the research conducted is that a novel method of crawling that extracts all social interactions from Facebook is presented. Over the period of the last few years, we have collected 280 million posts from public pages on Facebook using this crawling method. The collected posts include 35 billion likes and 5 billion comments from 700 million users. The data collection is the largest research dataset of social interactions on Facebook, enabling further and more accurate research in the area of social network analysis. With the extracted data, it is possible to illustrate interactions between different users that do not necessarily have to be connected. Methods using the same data to identify and cluster different opinions in online communities have also been developed and evaluated. Furthermore, a proposed method is used and validated for finding appropriate seeds for information cascade analyses, and identification of influential users. Based upon the conducted research, it appears that the data mining approach, association rule learning, can be used successfully in identifying influential users with high accuracy. In addition, the same method can also be used for identifying seeds in an information cascade setting, with no significant difference than other network-based methods. Finally, privacy-related consequences of posting online is an important area for users to consider. Therefore, mitigating privacy risks contributes to a secure environment and methods to protect user privacy are presented.
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Morini, Matteo. "Tools for Understanding the Dynamics of Social Networks." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEN075/document.
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Cette thèse fournit au lecteur un recueil d'applications de la théorie des graphes ; à ce but, des outils sur mesure, adaptés aux applications considérées, ont été conçus et mis en œuvre de manière inspirée par les données.Dans la première partie, une nouvelle métrique de centralité, nommée “bridgeness”, est présentée, basée sur une décomposition de la centralité intermédiaire (“betweenness centrality”) standard. Une composante, la “connectivité locale”, correspondante approximativement au degré d'un noeud, est différenciée de l'autre, qui, en revanche, évalue les propriétés structurelles à longue distance. En effet, cette dernière fournit une mesure de l'efficacité de chaque noeud à “relayer” parties faiblement connectées d'un réseau ; une caractéristique importante de cette métrique est son agnosticisme en ce qui concerne la structure de la communauté sous jacente éventuelle.Une deuxième application vise à décrire les caractéristiques dynamiques des graphes temporels qui apparaissent au niveau mésoscopique. L'ensemble de données de choix comprend 40 ans de publications scientifiques sélectionnées. L'apparition et l'évolution dans le temps d'un domaine d'étude spécifique (les ondelettes) sont capturées, en discriminant les caractéristiques persistantes des artefacts transitoires résultants du processus de détection des communautés, intrinsèquement bruité, effectué indépendamment sur des instantanées statiques successives. La notion de “flux laminaire”, sur laquelle repose le “score de complexité” que nous cherchons à optimiser, est présentée.Dans le même ordre d'idées, un réseau d'investisseurs japonais a été construit, sur la base d'un ensemble de données qui comprend des informations (indirectes) sur les filiales étrangères en copropriété. Une question très débattue dans le domaine de l'économie industrielle, l'hypothèse de Miwa-Ramseyer, a été démontrée de manière concluante comme fausse, du moins sous sa forme forte<br>This thesis provides the reader with a compendium of applications of network theory; tailor-madetools suited for the purpose have been devised and implemented in a data-driven fashion. In the first part, a novel centrality metric, aptly named “bridgeness”, is presented, based on adecomposition of the standard betweenness centrality. One component, local connectivity, roughlycorresponding to the degree of a node, is set apart from the other, which evaluates longer-rangestructural properties. Indeed, the latter provides a measure of the relevance of each node in“bridging” weakly connected parts of a network; a prominent feature of the metric is its agnosticism with regard to the eventual ground truth community structure.A second application is aimed at describing dynamic features of temporal graphs which are apparent at the mesoscopic level. The dataset of choice includes 40 years of selected scientific publications.The appearance and evolution in time of a specific field of study (“wavelets”) is captured,discriminating persistent features from transient artifacts, which result from the intrinsically noisy community detection process, independently performed on successive static snapshots. The concept of “laminar stream”, on which the “complexity score” we seek to optimize is based, is introduced.In a similar vein, a network of Japanese investors has been constructed, based on a dataset which includes (indirect) information on co-owned overseas subsidiaries. A hotly debated issue in the field of industrial economics, the Miwa-Ramseyer hypothesis, has been conclusively shown to be false, at least in its strong form
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DILIBERTO, Simona. "Households and their Expenditures as an Evolving Complex Social System." Doctoral thesis, Università degli Studi di Palermo, 2020. http://hdl.handle.net/10447/395284.
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Friggeri, Adrien. "A Quantitative Theory of Social Cohesion." Phd thesis, Ecole normale supérieure de lyon - ENS LYON, 2012. http://tel.archives-ouvertes.fr/tel-00737199.
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Community, a notion transversal to all areas of Social Network Analysis, has drawn tremendous amount of attention across the sciences in the past decades. Numerous attempts to characterize both the sociological embodiment of the concept as well as its observable structural manifestation in the social network have to this date only converged in spirit. No formal consensus has been reached on the quantifiable aspects of community, despite it being deeply linked to topological and dynamic aspects of the underlying social network. Presenting a fresh approach to the evaluation of communities, this thesis introduces and builds upon the cohesion, a novel metric which captures the intrinsic quality, as a community, of a set of nodes in a network. The cohesion, defined in terms of social triads, was found to be highly correlated to the subjective perception of communitiness through the use of a large-scale online experiment in which users were able to compute and rate the quality of their social groups on Facebook. Adequately reflecting the complexity of social interactions, the problem of finding a maximally cohesive group inside a given social network is shown to be NP-hard. Using a heuristic approximation algorithm, applications of the cohesion to broadly different use cases are highlighted, ranging from its application to network visualization, to the study of the evolution of agreement groups in the United States Senate, to the understanding of the intertwinement between subjects' psychological traits and the cohesive structures in their social neighborhood. The use of the cohesion proves invaluable in that it offers non-trivial insights on the network structure and its relation to the associated semantic.
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Martins, Rafael Franco. "Uma proposta de modelagem por agentes para o problema da herança e desigualdade econômica." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/100/100132/tde-12042017-111602/.
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Nos últimos anos a preocupação com uma possível elevação da desigualdade social aumentou consideravelmente, principalmente por conta da obra de Piketty intitulada O Capital no século XXI. Para Piketty a diminuição da natalidade entre os ricos será um dos pontos cruciais para este aumento. As previsões contidas no livro são avassaladoras, se nada for feito a desigualdade aumentará a ponto de ameaçar a democracia e os sistemas políticos e sociais vigentes. Porém, alguns economistas, como por exemplo: Matthew Rognlie, não concordam com esta tese e apontam supostos erros e falhas cometidos pelo economista francês. O intuito deste trabalho é tentar um caminho diferente do trilhado por Piketty, utilizando outros meios e técnicas, este trabalho se propõem a analisar a questão da desigualdade causada pela herança dos bens. Para tanto, foi implementado e adaptado o algoritmo descrito no artigo A family-network model for wealth distribution in societies para analisar esta questão à luz de técnicas e métodos como: monte carlo, grafos e simulações computacionais. O resultado obtido vai ao encontro dos resultados obtidos por Piketty<br>In recent years a concern for a possible rise in social inequality has increased considerably, especially on account of Piketty\'s work entitled \"Capital in the 21st Century.\" For a small reduction in the birth rate among the rich, it will be one of the crucial points for this increase. As predictions contained in the book are overwhelming, there is nothing for an inequality will increase a threatening point to democracy and existing political and social systems. However, some economists, such as Matthew Rognlie, do not agree with this article and point out supposed errors and failures committed by the French economist. The purpose of this paper is to try a different path from Piketty, using other means and techniques, this work is useful for an analysis of the issue of inequality caused by the inheritance of goods. A model of family network for the distribution of wealth in societies \"to a model of family network for the distribution of wealth in societies. The result obtained is in line with the results obtained by Piketty
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Araújo, Eduardo Barbosa. "Scientific Collaboration Networks from Lattes Database: Topology, Dynamics and Gender Statistics." reponame:Repositório Institucional da UFC, 2016. http://www.repositorio.ufc.br/handle/riufc/18489.
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ARAÚJO, Eduardo Barbosa. Scientific Collaboration Networks from Lattes Database: Topology, Dynamics and Gender Statistics. 2016. 88 f. Tese (Doutorado em Física) - Programa de Pós-Graduação em Física, Departamento de Física, Centro de Ciências, Universidade Federal do Ceará, Fortaleza, 2016.<br>Submitted by Edvander Pires (edvanderpires@gmail.com) on 2016-07-19T15:58:54Z
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Previous issue date: 2016<br>Understanding the dynamics of research production and collaboration may reveal better strategies for scientific careers, academic institutions and funding agencies. Here we propose the use of a large and multidisciplinary database of scientific curricula in Brazil, namely, the Lattes Platform, to study patterns of scientific production and collaboration. Detailed information about publications and researchers is available in this database. Individual curricula are submitted by the researchers themselves so that co-authorship is unambiguous. Researchers can be evaluated by scientific productivity, geographical location and field of expertise. Our results show that the collaboration network is growing exponentially for the last three decades, with a distribution of number of collaborators per researcher that approaches a power-law as the network gets older. Moreover, both the distributions of number of collaborators and production per researcher obey power-law behaviors, regardless of the geographical location or field, suggesting that the same universal mechanism might be responsible for network growth and productivity. We also show that the collaboration network under investigation displays a typical assortative mixing behavior, where teeming researchers (i.e., with high degree) tend to collaborate with others alike. Moreover, we discover that on average men prefer collaborating with other men than with women, while women are more egalitarian. This is consistently observed over all fields and essentially independent on the number of collaborators of the researcher. The solely exception is for engineering, where clearly this gender bias is less pronounced, when the number of collaborators increases. We also find that the distribution of number of collaborators follows a power-law, with a cut-off that is gender dependent. This reflects the fact that on average men produce more papers andhave more collaborators than women. We also find that both genders display the same tendency towards interdisciplinary collaborations, except for Exact and Earth Sciences, where women having many collaborators are more open to interdisciplinary research.<br>Compreender a dinâmica de produção e colaboração em pesquisa pode revelar melhores estratégias para carreiras científicas, instituições acadêmicas e agências de fomento. Neste trabalho nós propomos o uso de uma grande e multidisciplinar base de currículos científicos brasileira, a Plataforma Lattes, para o estudo de padrões em pesquisa científica e colaborações. Esta base de dados inclui informações detalhadas acerca de publicações e pesquisadores. Currículos individuais são enviados pelos próprios pesquisadores de forma que a identificação de coautoria não é ambígua. Pesquisadores podem ser classificados por produção científica, localização geográfica e áreas de pesquisa. Nossos resultados mostram que a rede de colaborações científicas tem crescido exponencialmente nas últimas três décadas, com a distribuição do número de colaboradores por pesquisador se aproximando de uma lei de potência à medida que a rede evolui. Além disso, ambas a distribuição do número de colaboradores e a produção por pesquisador seguem o comportamento de leis de potência, independentemente da região ou áreas, sugerindo que um mesmo mecanismo universal pode ser responsável pelo crescimento da rede e pela produtividade dos pesquisadores. Também mostramos que as redes de colaboração investigadas apresentam um típico comportamento assortativo, no qual pesquisadores de alto nível (com muitos colaboradores) tendem a colaborador com outros semelhantes. Em seguida, mostramos que homens preferem colaborar com outros homens enquanto mulheres são mais igualitárias ao estabelecer suas colaborações. Isso é consistentemente observado em todas as áreas e é essencialmente independente do número de colaborações do pesquisador. A única exceção sendo a área de Engenharia, na qual este viés é claramente menos pronunciado para pesquisadores com muitas colaborações. Também mostramos que o número de colaborações segue o comportamento de leis de potência, com um cutoff dependente do gênero. Isso se reflete no fato de que em média mulheres produzem menos artigos e têm menos colaborações que homens. Também mostramos que ambos os gêneros exibem a mesma tendência quanto a colaborações interdisciplinares, exceto em Ciências Exatas e da Terra, nas quais mulheres tendo mais colaboradores são mais propensas a pesquisas interdisciplinares.
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Kasper, Eric Calvin. "Nurturing emergent agency : networks and dynamics of complex social change processes in Raipur, India." Thesis, University of Sussex, 2017. http://sro.sussex.ac.uk/id/eprint/66943/.
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This thesis takes up the question, how can agency for people living in informal settlements be strengthened? To address this question, I carried out systemic action research with two NGO partners and residents from seven informal settlements in Raipur, India. This involved organizing ‘slum improvement committees' (SICs) in each of the seven settlements and carrying out joint actions in support of housing rights and implementation of the Rajiv Awas Yojana (RAY) housing policy. The data on which my analysis is based includes over one hundred conversations between myself and the project participants (both from the settlements as well as the partner NGOs), records of two public events, a social network survey of 46 people living in the participating settlements, a separate set of 9 participatory social network maps (NetMaps), and over two hundred pages of my own field notes based on my observations and participation in the research activities. My thesis makes an original contribution to the study of community agency by analysing it through the lens of complex systems theories and utilising the tools of social network analysis. My thesis also makes an original contribution to research methodology by making the technical analysis participatory, accessible, and useful for the participants. This allowed me to combine analysis of relational structures (social networks) with relational dynamics to show how significant social change happened over the course of the project. My thesis suggests that agency can be strengthened through an organizing practice that brings NGOs, academic researchers, and residents of informal settlements together to build relational power, take collective action, and create social change.
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Godoy, Lorite Antonia. "Time Evolution and Predictability of Social Behavior in Techno-Social Networks." Doctoral thesis, Universitat Rovira i Virgili, 2016. http://hdl.handle.net/10803/348873.
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El fet que cada vegada disposem de més dades socials de sistemes socio-tecnològics---sistemes que registren la nostra activitat diària, tals com a registres de targeta de crèdit, registres de trucades telefòniques, correu electrònic, etc.---i les xarxes socials on-line---com facebook, twitter, instagram, etc.---, ha fet possible estudiar el comportament humà des de diferents perspectives. Descobrir els patrons darrere d'aquestes dades no només aportarà un millor coneixement de la societat, sinó que també beneficiaria a la societat en diferents aspectes, com l'adaptació de tecnologia a les necessitats socials o el disseny de millors polítiques per evitar la propagació d'epidèmies. L'objectiu d'aquesta tesi és precisament descobrir patrons estructurals i temporals en els sistemes socials i desenvolupar models predictius sobre la seva base. En particular, analitzem l'evolució a llarg termini en una xarxa de correu electrònic amb més d'1.000 persones al llarg de quatre anys consecutius. Veiem que, encara que l'evolució de la comunicació entre usuaris és altament impredictible, l'evolució macro de les xarxes de comunicació social segueix lleis estadístiques ben definides, caracteritzades pel decaïment exponencial de les variacions logarítmicas del pes de les comunicacions entre usuaris i del pes dels individus a la xarxa. Al mateix temps, trobem que els individus tenen una forma característica de comunicar-se, i aquesta no canvia en anys. Quant a la predictabilidad, desenvolupem dos models basats en xarxes: un model de recomanació (que prediu votacions d'usuaris sobre objectes) i un model d'inferència temporal (que prediu successos en el temps). El nostre model de recomanació és escalable i considerablement més precís en les seves prediccions que els algorismes actuals per bases de dades de milions de votacions. L'enfocament es basa en la suposició que hi ha grups de persones i d'articles (per exemple, pel·lícules, llibres, etc.) i que les preferències d'un individu sobre un element donat depenen del grups als que pertanyin. Però a més, permet que cada individu i cada article pertanyin simultàniament a diferents grups. Les comunitats superposades resultants i les prediccions sobre les votacions poden inferir-se amb un algorisme escalable de maximització d'expectatives basat en una aproximació variacional. En el mo<br>El hecho que cada vez dispongamos de más datos sociales de sistemas socio-tecnológicos---sistemas que registran nuestra actividad diaria, tales como registros de tarjeta de crédito, registros de llamadas telefónicas, correo electrónico, etc.---y las redes sociales on-line---como facebook, twitter, instagram, etc.---, ha hecho posible estudiar el comportamiento humano desde diferentes perspectivas. Descubrir los patrones detrás de estos datos no sólo aportará un mejor conocimiento de la sociedad, sino que también beneficiaría a la sociedad en diferentes aspectos, como la adaptación de la tecnología a las necesidades sociales o el diseño de mejores políticas para evitar la propagación de epidemias. El objetivo de esta tesis es precisamente descubrir patrones estructurales y temporales en los sistemas sociales y desarrollar modelos predictivos en base a ellos. En particular, analizamos la evolución a largo plazo en una red de correo electrónico con más de 1.000 personas a lo largo de cuatro años consecutivos. Vemos que, aunque la evolución de la comunicación entre usuarios es altamente impredecible, la evolución macro de las redes de comunicación social sigue leyes estadísticas bien definidas, caracterizadas por el decaimiento exponencial de las variaciones logarítmicas del peso de las comunicaciones entre usuarios y del peso de los individuos en la red. Así mismo, encontramos que los individuos presentan una forma caracteristica de comunicarse, y esta no cambia en años. En cuanto a la predictibilidad, desarrollamos dos modelos basados en redes: un modelo de recomendación (que predice votaciones de usuarios sobre objetos) y un modelo de inferencia temporal (que predice sucesos en el tiempo). Nuestro modelo de recomendación es escalable y considerablemente más preciso en sus predicciones que los algoritmos actuales para bases de datos de millones de votaciones. El enfoque se basa en la suposición de que hay grupos de personas y de artículos (por ejemplo, películas, libros, etc.) y que las preferencias de un individuo sobre un artículo dado dependen de los grupos a los que pertenezcan. Pero además, permitimos que cada individuo y cada artículo pertenecan simultáneamente a diferentes grupos. Las comunidades superpuestas resultantes y las predicciones sobre las votaciones pueden inferirse con un algoritmo de maximiz<br>The increasing availability of social data sources from socio-technological systems ---systems that record our daily activity such as credit card records, call-phone records, email, etc.--- and on-line social networks ---such as facebook, twitter, instagram, etc.---, has made it possible to study human behavior from different perspectives. Uncovering the patterns behind this data would not only give us a better knowledge about our society but could also benefit our society in a number of ways such as adapting technology to social needs or design better policies to avoid spread of epidemics. The aim of this thesis is precisely to uncover both structural and temporal patterns in social systems and to develop predictive models based on them. In particular, we analyze the long-term evolution in an email network with over 1,000 individuals throughout four consecutive years. We find that, although the evolution of individual ties is highly unpredictable, the macro-evolution of social communication networks follows well-defined statistical laws, characterized by exponentially decaying log-variations of the weight of social ties and of individuals' social strength. At the same time, we find that individuals have social signatures that are remarkably stable over the scale of several years. Regarding predictability, we develop two network-based models: a recommender model, and a temporal inference model. Our recommender model makes scalable predictions and is considerably more accurate than current algorithms for large datasets. The approach is based on the assumption that there are groups of individuals and of items (e.g. movies, books, etc.), and that the preferences of an individual for an given item depend on their group memberships. Importantly, we allow each individual and each item to belong simultaneously to different groups. The resulting overlapping communities and the predicted preferences can be inferred with a scalable expectation-maximization algorithm based on a variational approximation. In the temporal inference model users can belong simultaneously to different groups, but also the time intervals belong to overlapping
communities. The results suggest that the algorithm is able to distinguish real events of non-events almost perfectly.
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Monti, C. C. "MODELING AND MINING COMPLEX NETWORKS WITH FEATURE-RICH NODES." Doctoral thesis, Università degli Studi di Milano, 2017. http://hdl.handle.net/2434/485805.
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Real-world complex networks describe connections between objects; in reality, those objects are typically endowed with features. How does the presence or absence of such features interplay with the network link structure? The idea is to be able to represent a wide range of scenarios — not only homophily and heterophily. In this work, as a first thing we will present an ad-hoc statistical model, showing it displays the same global topological properties of a real-world social network. Then, we will use this model to design and analyze learning algorithms for graph mining problems – such as predicting links, anomaly detection, discovering missing features, and so on. Finally, we will present some results on real complex networks of different kinds (citation networks and semantic networks).
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Orman, Keziban. "Contribution to the interpretation of evolving communities in complex networks : Application to the study of social interactions." Thesis, Lyon, INSA, 2014. http://www.theses.fr/2014ISAL0072/document.
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Les réseaux complexes constituent un outil pratique pour modéliser les systèmes complexes réels. Pour cette raison, ils sont devenus très populaires au cours de la dernière décennie. De nombreux outils existent pour étudier les réseaux complexes. Parmi ceux-ci, la détection de la communauté est l’un des plus importants. Une communauté est grossièrement définie comme un groupe de nœuds plus densément connectés entre eux qu’avec le reste du réseau. Dans la littérature, cette définition intuitive a été formalisée de plusieurs différentes façons, ce qui a conduit à d’innombrables méthodes et variantes permettant de les détecter. Du point de vue applicatif, le sens des communautés est aussi important que leur détection. Cependant, bien que la tâche de détection de communautés en elle-même ait attiré énormément d’attention, le problème de leur interprétation n’a pas été sérieusement abordé jusqu’à présent. Dans cette thèse, nous voyons l’interprétation des communautés comme un problème indépendant du processus de leur détection, consistant à identifier les éléments leurs caractéristiques les plus typiques. Nous le décomposons en deux sous-problèmes : 1) trouver un moyen approprié pour représenter une communauté ; et 2) sélectionner de façon objective les parties les plus caractéristiques de cette représentation. Pour résoudre ces deux sous-problèmes, nous exploitons l’information encodée dans les réseaux dynamiques attribués. Nous proposons une nouvelle représentation des communautés sous la forme de séquences temporelles de descripteurs associés à chaque nœud individuellement. Ces descripteurs peuvent être des mesures topologiques et des attributs nodaux. Nous détectons ensuite les motifs séquentiels émergents dans cet ensemble de données, afin d’identifier les ceux qui sont les plus caractéristiques de la communauté. Nous effectuons une validation de notre procédé sur des réseaux attribués dynamiques générés artificiellement. A cette occasion, nous étudions son comportement relativement à des changements structurels de la structure de communautés, à des modifications des valeurs des attributs. Nous appliquons également notre procédé à deux systèmes du monde réel : un réseau de collaborations scientifiques issu de DBLP, et un réseau d’interactions sociales et musicales tiré du service LastFM. Nos résultats montrent que les communautés détectées ne sont pas complètement homogènes. Certaines communautés sont composées de petits groupes de nœuds qui ont tendance à évoluer ensemble au cours du temps, que ce soit en termes de propriétés individuelles ou collectives. Les anomalies détectées correspondent généralement à des profils typiques : nœuds mal placés par l’outil de détection de communautés, ou nœuds différant des tendances de leur communautés sur certains points, et/ou non-synchrones avec l’évolution de leur communauté, ou encore nœuds complètement différents<br>Complex Networks constitute a convenient tool to model real-world complex systems. For this reason, they have become very popular in the last decade. Many tools exist to study complex networks. Among them, community detection is one of the most important. A community is roughly defined as a group of nodes more connected internally than to the rest of the network. In the literature, this intuitive definition has been formalized in many ways, leading to countless different methods and variants to detect communities. In the large majority of cases, the result of these methods is set of node groups in which each node group corresponds to a community. From the applicative point of view, the meaning of these groups is as important as their detection. However, although the task of detecting communities in itself took a lot of attraction, the problem of interpreting them has not been properly tackled until now. In this thesis, we see the interpretation of communities as a problem independent from the community detection process, consisting in identifying the most characteristic features of communities. We break it down into two sub-problems: 1) finding an appropriate way to represent a community and 2) objectively selecting the most characteristic parts of this representation. To solve them, we take advantage of the information encoded in dynamic attributed networks. We propose a new representation of communities under the form of temporal sequences of topological measures and attribute values associated to individual nodes. We then look for emergent sequential patterns in this dataset, in order to identify the most characteristic community features. We perform a validation of our framework on artificially generated dynamic attributed networks. At this occasion, we study its behavior relatively to changes in the temporal evolution of the communities, and to the distribution and evolution of nodal features. We also apply our framework to real-world systems: a DBLP network of scientific collaborations, and a LastFM network of social and musical interactions. Our results show that the detected communities are not completely homogeneous, in the sense several node topic or interests can be identified for a given community. Some communities are composed of smaller groups of nodes which tend to evolve together as time goes by, be it in terms of individual (attributes, topological measures) or relational (community migration) features. The detected anomalies generally fit some generic profiles: nodes misplaced by the community detection tool, nodes relatively similar to their communities, but also significantly different on certain features and/or not synchronized with their community evolution, and finally nodes with completely different interests
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Kolgushev, Oleg. "Influence of Underlying Random Walk Types in Population Models on Resulting Social Network Types and Epidemiological Dynamics." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc955128/.
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Epidemiologists rely on human interaction networks for determining states and dynamics of disease propagations in populations. However, such networks are empirical snapshots of the past. It will greatly benefit if human interaction networks are statistically predicted and dynamically created while an epidemic is in progress. We develop an application framework for the generation of human interaction networks and running epidemiological processes utilizing research on human mobility patterns and agent-based modeling. The interaction networks are dynamically constructed by incorporating different types of Random Walks and human rules of engagements. We explore the characteristics of the created network and compare them with the known theoretical and empirical graphs. The dependencies of epidemic dynamics and their outcomes on patterns and parameters of human motion and motives are encountered and presented through this research. This work specifically describes how the types and parameters of random walks define properties of generated graphs. We show that some configurations of the system of agents in random walk can produce network topologies with properties similar to small-world networks. Our goal is to find sets of mobility patterns that lead to empirical-like networks. The possibility of phase transitions in the graphs due to changes in the parameterization of agent walks is the focus of this research as this knowledge can lead to the possibility of disruptions to disease diffusions in populations. This research shall facilitate work of public health researchers to predict the magnitude of an epidemic and estimate resources required for mitigation.
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Livelsberger, Tara L. ""Lost" in conversations complex social behavior in Online environments /." Ohio : Ohio University, 2009. http://www.ohiolink.edu/etd/view.cgi?ohiou1244226331.
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Mina, Christakis. "Open Technological Standardization Processes Through Learning Networks." 京都大学 (Kyoto University), 2010. http://hdl.handle.net/2433/120839.
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Srivastava, Sameer Bhatt. "Social Capital Activation during Times of Organizational Change." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10158.
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This dissertation contributes to our understanding of how people build and use social capital – resources embedded in social relations – in organizational settings. Whereas the extant literature has tended to focus on the structure of interpersonal networks within organizations and the link to various indicators of individual attainment, this dissertation instead uncovers the dynamics of network action. I tackle two central questions: (1) During times of organizational change, how do organizational actors use the social resources accessible to them by virtue of their position in the structure? and (2) What organizational interventions can help people forge valuable new connections in the workplace? Core to this investigation is the concept of social capital activation – that is, the conversion of latent social ties into active relationships. Three empirical studies illuminate different facets of social capital activation during commonly experienced forms of organizational change: (1) an organizational restructuring; (2) large-scale transformations that create individual-level threat or opportunity; and (3) the introduction of a novel employee cross-training program. Because organizational change is often accompanied by significant shifts in resources and power, network activation choices in these periods can have significant consequences for individual attainment and organizational performance. I draw on unique data from three disparate settings – a global information services firm; a large health care organization; and a software development lab based in Beijing, China. Multiple research methods, including a large panel data set of archived electronic communications, qualitative interviews, experimental studies conducted with samples of working professionals, and a longitudinal field experiment, are used to identify how organizational actors marshal social resources through individual-level network activation choices. Findings from these studies contribute to research on: (1) organizational social capital; (2) the structural dynamics of organizational change; (3) ascriptive inequality in organizations; (4) cognition and social networks; and (5) workplace practices and network change.
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Mass, Lena M. "Analysing technology & innovation in complex networks : processes, dynamics, and development of multi-level interorganisational networks." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:a0280ae4-0523-4b23-8f4d-5e4f880fdcb1.
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There is still very little known about network dynamics (Bell et al., 2006), especially when focusing on interorganisational networks (Provan et al., 2007). There is also limited empirical evidence on leadership within these complex network contexts (Davenport, 2005; Osborn et al., 2002). This thesis addresses these limitations by developing a theoretical framework for process leadership in the complex, often unpredictable and turbulent context of the interorganisational networked ecosystem. Understanding the complexity of networks and leadership is crucial to advancing network research, which this study aims to accomplish. Although previous studies indicate leader characteristics and behaviours (Huxham & Vangen, 2000), less evidence on the processes and dynamics of leadership within networks exists. Few studies have longitudinally examined the multiple boundaries and multi-level interactions within a complex interorganisational network, as the unit of analysis, as this thesis achieves. Moreover, little research has been conducted to understand network leadership processes, which represents a major gap in the network theory and complexity leadership literatures. In order to address these gaps as well as the gap between the two literatures, this thesis presents a comprehensive, longitudinal case investigation of network process leadership (NPL) within an interorganisational network embedded in the British National Health Service (NHS). By analysing processual dynamics, this thesis’s contribution is the foundation of a preliminary NPL framework. Based on analysing a public sector healthcare network over time, the findings emphasise four dominant thematic constructs surrounding NPL that emerged as highly significant: leveraging strategic system stressors and turbulence; adopting focal and non-focal roles; maximising social proximity; and the complementary, reciprocal formal and informal coproduction of leadership. These constructs provide the empirical and analytical grounds to help explain the critical leadership processes that drive a complex, interorganisational public sector network. Significantly, social capital dimensions underlie these interrelated higher order themes; thereby affecting wider inter-organisational network processes. As a primary contribution of this thesis, I argue that social capital is the critical concept linking network and complexity leadership theories, in order to provide a better understanding of NPL. The findings suggest network leadership calls for NPL and its relational, collective, facilitative approach involving social capital among multiple participants in a complex interorganisational network context. This is highly differentiated from studying unidirectional effects of a hierarchical, central leader within a single organisation. Theoretically, I argue the importance of social capital in the complex nature of leadership processes within interorganisational networked contexts. The research contributes to an understanding of how networks and social capital can be adapted or created by formal and informal leaders within networks to reflect changing processes to shape practices and network-wide development over time. Finally, I offer several operational mechanisms policymakers and network leaders could pragmatically employ to manage, lead, and facilitate interorganisational network processes. Overall, the significance of this study involves: filling gaps in the literature, offering a longitudinal case study on an interorganisational network over time, providing a foundation for theoretical development on leading in networks, illuminating insights into professional leadership within networks, and identifying policy and practical implications for leaders and managers.
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Santos, Francisco C. "Topological evolution: from biological to social networks." Doctoral thesis, Universite Libre de Bruxelles, 2007. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210702.
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Peter, Camaren. "Bayesian participatory-based decision analysis : an evolutionary, adaptive formalism for integrated analysis of complex challenges to social-ecological system sustainability." Doctoral thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/18284.
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Includes bibliographical references (pages. 379-400).<br>This dissertation responds to the need for integration between researchers and decision-makers who are dealing with complex social-ecological system sustainability and decision-making challenges. To this end, we propose a new approach, called Bayesian Participatory-based Decision Analysis (BPDA), which makes use of graphical causal maps and Bayesian networks to facilitate integration at the appropriate scales and levels of descriptions. The BPDA approach is not a predictive approach, but rather, caters for a wide range of future scenarios in anticipation of the need to adapt to unforeseeable changes as they occur. We argue that the graphical causal models and Bayesian networks constitute an evolutionary, adaptive formalism for integrating research and decision-making for sustainable development. The approach was implemented in a number of different interdisciplinary case studies that were concerned with social-ecological system scale challenges and problems, culminating in a study where the approach was implemented with decision-makers in Government. This dissertation introduces the BPDA approach, and shows how the approach helps identify critical cross-scale and cross-sector linkages and sensitivities, and addresses critical requirements for understanding system resilience and adaptive capacity.
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Dugué, Nicolas. "Analyse du capitalisme social sur Twitter." Thesis, Orléans, 2015. http://www.theses.fr/2015ORLE2081/document.
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Le sociologue Bourdieu définit le capital social comme : "L’ensemble des ressources actuelles ou potentielles qui sont liées à la possession d’un réseau durable de relations". Sur Twitter, les abonnements, mentions et retweets créent un réseau de relations pour chaque utilisateur dont les ressources sont l’obtention d’informations pertinentes, la possibilité d’être lu, d’assouvir un besoin narcissique, de diffuser efficacement des messages.Certains utilisateurs Twitter -appelés capitalistes sociaux - cherchent à maximiser leur nombre d’abonnements pour maximiser leur capital social. Nous introduisons leurs techniques, basées sur l’échange d’abonnements et l’utilisation de hashtags dédiés. Afin de mieux les étudier, nous détaillons tout d’abord une méthode pour détecter à l’échelle du réseau ces utilisateurs en se basant sur leurs abonnements et abonnés. Puis, nous montrons avec un compte Twitter automatisé que ces techniques permettent de gagner efficacement des abonnés et de se faire beaucoup retweeter. Nous établissons ensuite que ces dernières permettent également aux capitalistes sociaux d’occuper des positions qui leur accordent une bonne visibilité dans le réseau. De plus, ces méthodes rendent ces utilisateurs influents aux yeux des principaux outils de mesure. Nous mettons en place une méthode de classification supervisée pour détecter avec précision ces utilisateurs et ainsi produire un nouveau score d’influence<br>Bourdieu, a sociologist, defines social capital as : "The set of current or potential ressources linked to the possession of a lasting relationships network". On Twitter,the friends, followers, users mentionned and retweeted are considered as the relationships network of each user, which ressources are the chance to get relevant information, to beread, to satisfy a narcissist need, to spread information or advertisements. We observethat some Twitter users that we call social capitalists aim to maximize their follower numbers to maximize their social capital. We introduce their methods, based on mutual subscriptions and dedicated hashtags. In order to study them, we first describe a large scaledetection method based on their set of followers and followees. Then, we show with an automated Twitter account that their methods allow to gain followers and to be retweeted efficiently. Afterwards, we bring to light that social capitalists methods allows these users to occupy specific positions in the network allowing them a high visibility.Furthermore, these methods make these users influent according to the major tools. Wethus set up a classification method to detect accurately these user and produce a newinfluence score
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AraÃjo, Eduardo Barbosa. "Scientific Collaboration Networks from Lattes Database: Topology, Dynamics and Gender Statistics." Universidade Federal do CearÃ, 2016. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=17184.
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Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico<br>Compreender a dinÃmica de produÃÃo e colaboraÃÃo em pesquisa pode revelar melhores estratÃgias para carreiras cientÃficas, instituiÃÃes acadÃmicas e agÃncias de fomento. Neste trabalho nÃs propomos o uso de uma grande e multidisciplinar base de currÃculos cientÃficos brasileira, a Plataforma Lattes, para o estudo de padrÃes em pesquisa cientÃfica e colaboraÃÃes. Esta base de dados inclui informaÃÃes detalhadas acerca de publicaÃÃes e pesquisadores. CurrÃculos individuais sÃo enviados pelos prÃprios pesquisadores de forma que a identificaÃÃo de coautoria nÃo à ambÃgua. Pesquisadores
podem ser classificados por produÃÃo cientÃfica, localizaÃÃo geogrÃfica e Ãreas de pesquisa. Nossos resultados mostram que a rede de colaboraÃÃes cientÃficas tem crescido exponencialmente nas Ãltimas trÃs dÃcadas, com a
distribuiÃÃo do nÃmero de colaboradores por pesquisador se aproximando de uma lei de potÃncia à medida que a rede evolui. AlÃm disso, ambas a distribuiÃÃo do nÃmero de colaboradores e a produÃÃo por pesquisador seguem o comportamento de leis de potÃncia, independentemente da regiÃo ou Ãreas, sugerindo que um mesmo mecanismo universal pode ser responsÃvel pelo crescimento da rede e pela
produtividade dos pesquisadores. TambÃm mostramos que as redes de colaboraÃÃo investigadas apresentam um tÃpico comportamento assortativo, no qual pesquisadores de alto nÃvel (com muitos colaboradores) tendem a colaborador com outros semelhantes. Em seguida, mostramos que homens preferem colaborar com outros homens enquanto mulheres sÃo mais igualitÃrias ao estabelecer suas colaboraÃÃes. Isso à consistentemente observado em todas as Ãreas e Ã
essencialmente independente do nÃmero de colaboraÃÃes do pesquisador. A Ãnica exceÃÃo sendo a Ãrea de Engenharia, na qual este viÃs à claramente menos pronunciado para pesquisadores com muitas colaboraÃÃes. TambÃm mostramos que o nÃmero de colaboraÃÃes segue o comportamento de leis de potÃncia, com um cutoff dependente do gÃnero. Isso se reflete no fato de que em mÃdia mulheres produzem menos artigos e tÃm menos colaboraÃÃes que homens. TambÃm mostramos que ambos os gÃneros exibem a mesma tendÃncia quanto a colaboraÃÃes interdisciplinares, exceto em CiÃncias Exatas e da Terra, nas quais mulheres tendo mais colaboradores sÃo mais propensas a pesquisas interdisciplinares.<br>Understanding the dynamics of research production and collaboration may reveal better strategies for scientific careers, academic institutions and funding agencies. Here we propose the use of a large and multidisciplinary database of
scientific curricula in Brazil, namely, the Lattes Platform, to study patterns of scientific production and collaboration. Detailed information about publications and researchers is available in this database. Individual curricula are submitted by the researchers themselves so that co-authorship is unambiguous. Researchers can be evaluated by scientific productivity, geographical location and field of expertise. Our results show that the collaboration network is growing exponentially for the last three decades, with
a distribution of number of collaborators per researcher that approaches a power-law as the network gets older. Moreover, both the distributions of number of collaborators and production per researcher obey power-law behaviors,
regardless of the geographical location or field, suggesting that the same universal mechanism might be responsible for network growth and productivity. We also show that the collaboration network under investigation displays a typical
assortative mixing behavior, where teeming researchers (i.e., with high degree) tend to collaborate with others alike. Moreover, we discover that on average men prefer collaborating with other men than with women, while women are
more egalitarian. This is consistently observed over all fields and essentially independent on the number of collaborators of the researcher. The solely exception is for engineering, where clearly this gender bias is less pronounced, when the number of collaborators increases. We also find that the distribution of number of collaborators follows a power-law, with a cut-off that is gender
dependent. This reflects the fact that on average men produce more papers andhave more collaborators than women. We also find that both genders display the same tendency towards interdisciplinary collaborations, except for Exact and Earth
Sciences, where women having many collaborators are more open to interdisciplinary research.
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Dulac, Adrien. "Etude des modèles à composition mixée pour l'analyse de réseaux complexes." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM080/document.
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Les données relationnelles sont omniprésentes dans la nature et leur accessibilité ne cesse d'augmenter depuis ces dernières années. Ces données, vues comme un tout, forment un réseau qui peut être représenté par une structure de données appelée graphe où chaque nœud du graphe est une entité et chaque arête représente une relation ou connexion entre ces entités. Les réseaux complexes en général, tels que le Web, les réseaux de communications ou les réseaux sociaux sont connus pour exhiber des propriétés structurelles communes qui émergent aux travers de leurs graphes. Dans cette thèse, nous mettons l'accent sur deux importantes propriétés appelées *homophilie* et *attachement préférentiel* qui se produisent dans un grand nombre de réseaux réels. Dans une première phase, nous étudions une classe de modèles de graphes aléatoires dans un contexte Bayésien non-paramétrique, appelé *modèle de composition mixée*, et nous nous concentrons à montrer si ces modèles satisfont ou non les propriétés mentionnées, après avoir proposé des définitions formelles pour ces dernières. Nous conduisons ensuite une évaluation empirique pour mettre à l'épreuve nos résultats sur des jeux de données de réseaux synthétiques et réels. Dans une seconde phase, nous proposons un nouveau modèle, qui généralise un précédent modèle à composition mixée stochastique, adapté pour les réseaux pondérés et nous développons un algorithme d'inférence efficace capable de s'adapter à des réseaux de grande échelle<br>Relational data are ubiquitous in the nature and their accessibility has not ceased to increase in recent years. Those data, see as a whole, form a network, which can be represented by a data structure called a graph, where each vertex of the graph is an entity and each edge a connection between pair of vertices. Complex networks in general, such as the Web, communication networks or social network, are known to exhibit common structural properties that emerge through their graphs. In this work we emphasize two important properties called *homophilly* and *preferential attachment* that arise on most of the real-world networks. We firstly study a class of powerful *random graph models* in a Bayesian nonparametric setting, called *mixed-membership model* and we focus on showing whether the models in this class comply with the mentioned properties, after giving formal definitions in a probabilistic context of the latter. Furthermore, we empirically evaluate our findings on synthetic and real-world network datasets. Secondly, we propose a new model, which extends the former Stochastic Mixed-Membership Model, for weighted networks and we develop an efficient inference algorithm able to scale to large-scale networks
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Martinez, MaryAnn. "Human Centeredness: The Foundation for Leadership-as-Practice in Complex Local/Regional Food Networks." Antioch University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=antioch1624179376157514.
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RADICIONI, Tommaso. "All the ties that bind. A socio-semantic network analysis of Twitter political discussions." Doctoral thesis, Scuola Normale Superiore, 2021. http://hdl.handle.net/11384/109224.
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Social media play a crucial role in what contemporary sociological reflections define as a ‘hybrid
media system’. Online spaces created by social media platforms resemble global public
squares hosting large-scale social networks populated by citizens, political leaders, parties
and organizations, journalists, activists and institutions that establish direct interactions and
exchange contents in a disintermediated fashion. In the last decade, an increasing number
of studies from researchers coming from different disciplines has approached the study of the
manifold facets of citizen participation in online political spaces. In most cases, these studies
have focused on the investigation of direct relationships amongst political actors. Conversely,
relatively less attention has been paid to the study of contents that circulate during online
discussions and how their diffusion contributes to building political identities. Even more
rarely, the study of social media contents has been investigated in connection with those concerning
social interactions amongst online users. To fill in this gap, my thesis work proposes
a methodological procedure consisting in a network-based, data-driven approach to both
infer communities of users with a similar communication behavior and to extract the most
prominent contents discussed within those communities. More specifically, my work focuses
on Twitter, a social media platform that is widely used during political debates. Groups
of users with a similar retweeting behavior - hereby referred to as discursive communities -
are identified starting with the bipartite network of Twitter verified users retweeted by nonverified
users. Once the discursive communities are obtained, the corresponding semantic
networks are identified by considering the co-occurrences of the hashtags that are present in
the tweets sent by their members.
The identification of discursive communities and the study of the related semantic networks
represent the starting point for exploring more in detail two specific conversations that took
place in the Italian Twittersphere: the former occured during the electoral campaign before
the 2018 Italian general elections and in the two weeks after Election day; the latter
centered on the issue of migration during the period May-November 2019. Regarding the
social analysis, the main result of my work is the identification of a behavior-driven picture
of discursive communities induced by the retweeting activity of Twitter users, rather than
determined by prior information on their political affiliation. Although these communities
do not necessarily match the political orientation of their users, they are closely related to
the evolution of the Italian political arena. As for the semantic analysis, this work sheds light
on the symbolic dimension of partisan dynamics. Different discursive communities are, in
fact, characterized by a peculiar conversational dynamics at both the daily and the monthly
time-scale. From a purely methodological aspect, semantic networks have been analyzed by
employing three (increasingly restrictive) benchmarks. The k-shell decomposition of both
filtered and non-filtered semantic networks reveals the presence of a core-periphery structure
providing information on the most debated topics within each discursive community and
characterizing the communication strategy of the corresponding political coalition.
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Rocha, Luis E. C. "Exploring patterns of empirical networks." Doctoral thesis, Umeå universitet, Institutionen för fysik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-46588.
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We are constantly struggling to understand how nature works, trying to identify recurrent events and looking for analogies and relations between objects or individuals. Knowing patterns of behavior is powerful and fundamental for survival of any species. In this thesis, datasets of diverse systems related to transportation, economics, sexual and social contacts, are characterized by using the formalisms of time series and network theory. Part of the results consists on the collection and analyzes of original network data, the rest focuses on the simulation of dynamical processes on these networks and to study how they are affected by the particular structures. The majority of the thesis is about temporal networks, i.e. networks whose structure changes in time. The new temporal dimension reveals structural dynamical properties that help to understand the feedback mechanisms responsible to make the network structure to adapt and to understand the emergence and inhibition of diverse phenomena in dynamic systems, as epidemics in sexual and contact networks.<br>Vi är ständigt kämpar för att förstå hur naturen fungerar, försöker identifier återkommande evenemang och söker analogier och relationer mellan objekt eller individer. Veta beteendemönster är kraftfull och grundläggande för överlevnad av arter. I denna avhandling, dataset av olika system i samband med transporter är ekonomi, sexuella och sociala kontakter, som kännetecknas av att använda formalismer av tidsserier och nätverk teori. En del av resultatet utgörs av insamling och analys av ursprungliga nätdata, fokuserar resten på simulering av dynamiska processer i dessa nätverk och att studera hur de påverkas av de särskilda strukturer. Huvuddelen av avhandlingen handlar om tidsmässiga nät, i.e. nät vars struktur förändringar i tid. Den nya tidsdimensionen avslöjar strukturella dynamiska egenskaper som hjälper till att förstå den feedback mekanismer som ansvarar för att göra nätverksstruktur att anpassa sig och förstå uppkomsten och hämning av olika företeelser i dynamiska system, epidemier i sexuella och kontaktnät.<br>Constantemente nos esforçamos para entender como a natureza funciona, tentando identificar eventos recorrentes e procurando por analogias e relações entre objetos ou indivíduos. Conhecer padrões de comportamento é algo poderoso e fundamental para a sobrevivência de qualquer espécie. Nesta tese, dados de sistemas diversos, relacionados a transporte, economia, contatos sexuais e sociais, são caracterizados usando o formalismo de séries temporais e teoria de redes. Uma parte dos resultados consiste na coleta e análise de dados de redes originais, a outra parte concentra-se na simulação de processos dinâmicos nessas redes e no estudo de como esses processos são afetados por determinadas estruturas. A maior parte da tese é sobre redes temporais, ou seja, redes cuja estrutura varia no tempo. A nova dimensão temporal revela propriedades estruturais dinâmicas que contribuem para o entendimento dos mecanismos de resposta responsáveis pela adaptação da rede, e para o entendimento da emergência e inibição de fenômenos diversos em sistemas dinâmicos, como epidemias em redes sexuais e de contato pessoal.
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De, Luca Giancarlo. "Decision Making in Complex Environments: an adaptive network approach." Doctoral thesis, SISSA, 2013. http://hdl.handle.net/20.500.11767/4808.
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In this thesis we investigate decision making in complex environments using
adaptive network models.
We first focus on the problem of consensus decision making in large animal
groups. Each individual has an internal state that models its choice among the
possible q alternatives and we assume that each individual updates its internal
state using a majority rule, if it is connected to other individuals, or using a
probabilistic rule. In this case, if the individual has no information, the choice
shall be totally random, otherwise the probabilistic rule shall have a bias toward
one of the q choices, measured by a parameter hi. The individuals shall also
update their neighbourhood adaptively, which is modelled by a link creation/
link destruction process with an effective rate z . We show that the system, if there
are no informed individuals, undergoes a I order phase transition at a give value,
∗z , between a disordered phase and a phase were consensus is reached. When
the number of informed individuals increases, the first order phase transition
remains, until one reaches a critical value of informed individuals above which
the system is no more critical. We also prove that, for z in a critical range, the
removal of knowledgeable individuals may induce a transition to a phase where
the group is no able to reach a consensual decision. We apply these results to
interpret some data on seasonal migrations of Atlantic Bluefin Tuna.
We, then, build a model to describe the emergence of hierarchical structures in
societies of rational self-interested agents. This model constitutes a highly stylised
model for human societies. The decision-making problem of the agents, in this
situation, is to which other agent to connect itself. We model the preference of
agents of that society for connecting to more prominent agents with a parameter
β. We show that there exists a sharp transition between a disordered equalitarian
society and an ordered hierarchical society as beta increases. Moreover, we
prove that, in a hierarchical society, social mobility is almost impossible, which
captures behaviours that have been observed in real societies.
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Cambe, Jordan. "Understanding the complex dynamics of social systems with diverse formal tools." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEN043/document.
Full textAbstract:
Au cours des deux dernières décennies les objets connectés ont révolutionné la traçabilité des phénomènes sociaux. Les trajectoires sociales laissent aujourd'hui des traces numériques, qui peuvent être analysées pour obtenir une compréhension plus profonde des comportements collectifs. L'essor de grands réseaux sociaux (comme Facebook, Twitter et plus généralement les réseaux de communication mobile) et d'infrastructures connectées (comme les réseaux de transports publiques et les plate-formes en ligne géolocalisées) ont permis la constitution de grands jeux de données temporelles. Ces nouveaux jeux de données nous donnent l'occasion de développer de nouvelles méthodes pour analyser les dynamiques temporelles de et dans ces systèmes.De nos jours, la pluralité des données nécessite d'adapter et combiner une pluralité de méthodes déjà existantes pour élargir la vision globale que l'on a de ces systèmes complexes. Le but de cette thèse est d'explorer les dynamiques des systèmes sociaux au moyen de trois groupes d'outils : les réseaux complexes, la physique statistique et l'apprentissage automatique. Dans cette thèse je commencerai par donner quelques définitions générales et un contexte historique des méthodes mentionnées ci-dessus. Après quoi, nous montrerons la dynamique complexe d'un modèle de Schelling suite à l'introduction d'une quantité infinitésimale de nouveaux agents et discuterons des limites des modèles statistiques. Le troisième chapitre montre la valeur ajoutée de l'utilisation de jeux de données temporelles. Nous étudions l'évolution du comportement des utilisateurs d'un réseau de vélos en libre-service. Puis, nous analysons les résultats d'un algorithme d'apprentissage automatique non supervisé ayant pour but de classer les utilisateurs en fonction de leurs profils. Le quatrième chapitre explore les différences entre une méthode globale et une méthode locale de détection de communautés temporelles sur des réseaux scientométriques. Le dernier chapitre combine l'analyse de réseaux complexes et l'apprentissage automatique supervisé pour décrire et prédire l'impact de l'introduction de nouveaux commerces sur les commerces existants. Nous explorons l'évolution temporelle de l'impact et montrons le bénéfice de l'utilisation de mesures de topologies de réseaux avec des algorithmes d'apprentissage automatique<br>For the past two decades, electronic devices have revolutionized the traceability of social phenomena. Social dynamics now leave numerical footprints, which can be analyzed to better understand collective behaviors. The development of large online social networks (like Facebook, Twitter and more generally mobile communications) and connected physical structures (like transportation networks and geolocalised social platforms) resulted in the emergence of large longitudinal datasets. These new datasets bring the opportunity to develop new methods to analyze temporal dynamics in and of these systems. Nowadays, the plurality of data available requires to adapt and combine a plurality of existing methods in order to enlarge the global vision that one has on such complex systems. The purpose of this thesis is to explore the dynamics of social systems using three sets of tools: network science, statistical physics modeling and machine learning. This thesis starts by giving general definitions and some historical context on the methods mentioned above. After that, we show the complex dynamics induced by introducing an infinitesimal quantity of new agents to a Schelling-like model and discuss the limitations of statistical model simulation. The third chapter shows the added value of using longitudinal data. We study the behavior evolution of bike sharing system users and analyze the results of an unsupervised machine learning model aiming to classify users based on their profiles. The fourth chapter explores the differences between global and local methods for temporal community detection using scientometric networks. The last chapter merges complex network analysis and supervised machine learning in order to describe and predict the impact of new businesses on already established ones. We explore the temporal evolution of this impact and show the benefit of combining networks topology measures with machine learning algorithms
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JAVARONE, MARCO ALBERTO. "Models and frameworks for studying social behaviors." Doctoral thesis, Università degli Studi di Cagliari, 2013. http://hdl.handle.net/11584/266244.
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Studies on social systems and human behavior are typically considered domain of humanities and psychology. However, it appears that recently these issues have attracted a strong interest also from the scienti�c community belonging to the hard sciences {in particular from physics, computer science
and mathematics. The network theory o�ers powerful tools to study social systems and human behavior. In particular, complex networks have gained a lot of prestige as general framework for representing and analyze real systems.
From an historical perspective, complex networks are rooted in graph theory {which in turn is dated back to 1736, when Leonhard Euler wrote the paper on the seven bridges of K�onigsberg. After Euler's work, di�erent mathematicians (e.g. Cayley) focused their research on graphs {opening the
possibility of applying their results to deal with theoretical and real problems. As a result, complex networks emerged as multidisciplinary approach
for studying complex systems. From a computational perspective, models based on complex networks allows to extract information on complex systems composed by a great number of interacting elements. A variety of systems
can be modelled as a complex network (e.g. social networks, the World Wide Web, internet, biological systems, and ecological systems). To summarize, any such system should give the possibility of viewing its elements as
simple (at some degree of abstraction), while assuming the existence of nonlinear interactions, the absence of a central control, and emergent behavior. Nowadays, scientists belonging to di�erent communities use complex networks as a framework for dealing with their preferred research issues, from a theoretical and/or pratical perspective. This work is aimed at illustrating
some models, based on complex networks, deemed useful to represent social behaviors like competitive dynamics, groups formation, and emergence of linguistics phenomena.
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Kurka, David Burth 1988. "Online social networks = knowledge extraction from information diffusion and analysis of spatio-temporal phenomena = Redes sociais online: extração de conhecimento e análise espaço-temporal de eventos de difusão de informação." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259074.
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Orientador: Fernando José Von Zuben<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação<br>Made available in DSpace on 2018-08-27T03:14:35Z (GMT). No. of bitstreams: 1
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Previous issue date: 2015<br>Resumo: Com o surgimento e a popularização de Redes Sociais Online e de Serviços de Redes Sociais, pesquisadores da área de computação têm encontrado um campo fértil para o desenvolvimento de trabalhos com grande volume de dados, modelos envolvendo múltiplos agentes e dinâmicas espaço-temporais. Entretanto, mesmo com significativo elenco de pesquisas já publicadas no assunto, ainda existem aspectos das redes sociais cuja explicação é incipiente. Visando o aprofundamento do conhecimento da área, este trabalho investiga fenômenos de compartilhamento coletivo na rede, que caracterizam eventos de difusão de informação. A partir da observação de dados reais oriundos do serviço online Twitter, tais eventos são modelados, caracterizados e analisados. Com o uso de técnicas de aprendizado de máquina, são encontrados padrões nos processos espaço-temporais da rede, tornando possível a construção de classificadores de mensagens baseados em comportamento e a caracterização de comportamentos individuais, a partir de conexões sociais<br>Abstract: With the advent and popularization of Online Social Networks and Social Networking Services, computer science researchers have found fertile field for the development of studies using large volumes of data, multiple agents models and spatio-temporal dynamics. However, even with a significant amount of published research on the subject, there are still aspects of social networks whose explanation is incipient. In order to deepen the knowledge of the area, this work investigates phenomena of collective sharing on the network, characterizing information diffusion events. From the observation of real data obtained from the online service Twitter, we collect, model and characterize such events. Finally, using machine learning and computational data analysis, patterns are found on the network's spatio-temporal processes, making it possible to classify a message's topic from users behaviour and the characterization of individual behaviour, from social connections<br>Mestrado<br>Engenharia de Computação<br>Mestre em Engenharia Elétrica
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MORENO, Bruno Neiva. "Representação e análise de encontros espaço-temporais publicados em redes sociais online." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/18621.
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Previous issue date: 2016-09-09<br>O crescente uso de redes sociais online tem feito com que usuários compartilhem, também, informações detalhadas a respeito dos locais que os mesmos frequentam, criando uma ligação entre o mundo físico (o movimento destes usuários no globo) e o mundo virtual (o que eles expressam sobre esses movimentos nas redes). O “check-in” é a funcionalidade responsável pelo compartilhamento da localização. Em uma rede social com essa funcionalidade, qualquer usuário pode publicar o local em que o mesmo está em determinado instante de tempo. Esta tese apresenta novas abordagens de análise de redes sociais online considerando as dimensões social, espacial e temporal que são inerentes à publicação de check-ins de usuários. As informações sociais, espaciais e temporais são definidas sob a perspectiva de encontros de usuários, sendo este o objeto de estudo dessa tese. Encontros ocorrem quando duas pessoas (dimensão social), estão em algum local (dimensão espacial), em determinado instante de tempo (dimensão temporal) e decidem publicar esse encontro através de check-ins. Além de apresentar um algoritmo para detecção de encontros, é definido um modelo para representação desses encontros. Este modelo é chamado de SiST (do inglês, SocIal, Spatial and Temporal) e modela encontros por meio de redes complexas. Para validar o modelo proposto, foram utilizados dados reais de redes sociais online. Com esses dados, os encontros foram detectados e analisados sob diferentes perspectivas com o objetivo de investigar a existência de alguma lei que governe a publicação dos mesmos, bem como para identificar padrões relativos a sua ocorrência, como padrões temporais, por exemplo. Além disso, as redes construídas a partir do modelo SiST também foram analisadas em termos de suas propriedades estruturais e topológicas. Por meio de redes SiST também foram estudados padrões de movimentação de usuários, como situações em que usuários se movimentam em grupo no globo ou situações em que um usuário é seguido por outros.<br>The growing use of online social networks has caused users to share detailed information about the places they visit, resulting on a clear connection between the physical world (i.e. the movement of these users on the globe) and the virtual world (which they express about these movements in the social network). The functionality responsible for sharing location by users is named as “check in”. In a social network with this feature, any user can publish their visited places. This thesis presents new approaches for online social networks analysis considering the social, spatial and temporal dimensions that are implicit in the publication of users check-ins. Social, spatial and temporal information is defined from the perspective of “user encounters”, which is the study object of this thesis. Users encounters occur when two people (social dimension) are somewhere (spatial dimension) in a given time (temporal dimension) and decide to publish this meeting through check-ins. In addition to the algorithm presented for encounters detection, we also defined a model for representation of these encounters. This model is called as SiST (SocIal, Spatial and Temporal). The SiST model basically represent encounters by a graph structure. To validate the proposed approach, we used real data from online social networks. With these data the users encounters were detected and analyzed from different perspectives aiming at investigating the existence of any law governing the publication of encounters and also to identify patterns related to its occurrence, like temporal patterns, for example. Furthermore, the graphs built from SiST model were also analyzed in terms of its structural and topological properties. Through the SiST networks the users movements were studied as well, like in situations in which users move in group or situations where users are followed by other users.
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Gómez, Cerdà Vicenç. "Algorithms and complex phenomena in networks: Neural ensembles, statistical, interference and online communities." Doctoral thesis, Universitat Pompeu Fabra, 2008. http://hdl.handle.net/10803/7548.
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Aquesta tesi tracta d'algoritmes i fenòmens complexos en xarxes.<br/><br/>En la primera part s'estudia un model de neurones estocàstiques inter-comunicades mitjançant potencials d'acció. Proposem una tècnica de modelització a escala mesoscòpica i estudiem una transició de fase en un acoblament crític entre les neurones. Derivem una regla de plasticitat sinàptica local que fa que la xarxa s'auto-organitzi en el punt crític.<br/><br/>Seguidament tractem el problema d'inferència aproximada en xarxes probabilístiques mitjançant un algorisme que corregeix la solució obtinguda via belief propagation en grafs cíclics basada en una expansió en sèries. Afegint termes de correcció que corresponen a cicles generals en la xarxa, s'obté el resultat exacte. Introduïm i analitzem numèricament una manera de truncar aquesta sèrie.<br/><br/>Finalment analizem la interacció social en una comunitat d'Internet caracteritzant l'estructura de la xarxa d'usuaris, els fluxes de discussió en forma de comentaris i els patrons de temps de reacció davant una nova notícia.<br>This thesis is about algorithms and complex phenomena in networks.<br/><br/>In the first part we study a network model of stochastic spiking neurons. We propose a modelling technique based on a mesoscopic description level and show the presence of a phase transition around a critical coupling strength. We derive a local plasticity which drives the network towards the critical point.<br/><br/>We then deal with approximate inference in probabilistic networks. We develop an algorithm which corrects the belief propagation solution for loopy graphs based on a loop series expansion. By adding correction terms, one for each "generalized loop" in the network, the exact result is recovered. We introduce and analyze numerically a particular way of truncating the series.<br/><br/>Finally, we analyze the social interaction of an Internet community by characterizing the structure of the network of users, their discussion threads and the temporal patterns of reaction times to a new post.
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Valejo, Alan Demetrius Baria. "Refinamento multinível em redes complexas baseado em similaridade de vizinhança." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-14042015-142526/.
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No contexto de Redes Complexas, particularmente das redes sociais, grupos de objetos densamente conectados entre si, esparsamente conectados a outros grupos, são denominados de comunidades. Detecção dessas comunidades tornou-se um campo de crescente interesse científico e possui inúmeras aplicações práticas. Nesse contexto, surgiram várias pesquisas sobre estratégias multinível para particionar redes com elevada quantidade de vértices e arestas. O objetivo dessas estratégias é diminuir o custo do algoritmo de particionamento aplicando-o sobre uma versão reduzida da rede original. Uma possibilidade dessa estratégia, ainda pouco explorada, é utilizar heurísticas de refinamento local para melhorar a solução final. A maioria das abordagens de refinamento exploram propriedades gerais de redes complexas, tais como corte mínimo ou modularidade, porém, não exploram propriedades inerentes de domínios específicos. Por exemplo, redes sociais são caracterizadas por elevado coeficiente de agrupamento e assortatividade significativa, consequentemente, maximizar tais características pode conduzir a uma boa solução e uma estrutura de comunidades bem definida. Motivado por essa lacuna, neste trabalho é proposto um novo algoritmo de refinamento, denominado RSim, que explora características de alto grau de transitividade e assortatividade presente em algumas redes reais, em particular em redes sociais. Para isso, adotou-se medidas de similaridade híbridas entre pares de vértices, que utilizam os conceitos de vizinhança e informações de comunidades para interpretar a semelhança entre pares de vértices. Uma análise comparativa e sistemática demonstrou que o RSim supera os algoritmos de refinamento habituais em redes com alto coeficiente de agrupamento e assortatividade. Além disso, avaliou-se o RSim em uma aplicação real. Nesse cenário, o RSim supera todos os métodos avaliado quanto a eficiência e eficácia, considerando todos os conjuntos de dados selecionados.<br>In the context of complex networks, particularly social networks, groups of densely interconnected objects, sparsely linked to other groups are called communities. Detection of these communities has become a field of increasing scientific interest and has numerous practical applications. In this context, several studies have emerged on multilevel strategies for partitioning networks with high amount of vertices and edges. The goal of these strategies is to reduce the cost of partitioning algorithm by applying it on a reduced version of the original network. The possibility for this strategy, yet little explored, is to apply local refinement heuristics to improve the final solution. Most refinement approaches explore general properties of complex networks, such as minimum cut or modularity, however, do not exploit inherent properties of specific domains. For example, social networks are characterized by high clustering coefficient and significant assortativity, hence maximize such characteristics may lead to a good solution and a well-defined community structure. Motivated by this gap, in this thesis, we propose a new refinement algorithm, called RSim, which exploits characteristics of high degree of transitivity and assortativity present in some real networks, particularly social networks. For this, we adopted hybrid similarity measures between pairs of vertices, using the concepts of neighborhood and community information to interpret the similarity between pairs of vertices. A systematic and comparative analysis showed that the RSim statistically outperforms usual refinement algorithms in networks with high clustering coefficient and assortativity. In addition, we assessed the RSim in a real application. In this scenario, the RSim surpasses all evaluated methods in efficiency and effectiveness, considering all the selected data sets.
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Müller-Hansen, Finn. "A complex systems perspective on land-use dynamics in the Amazon: patterns, agents, networks." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/19476.
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Die Doktorarbeit untersucht, wie sich Mensch-Umwelt-Interaktionen am Beispiel von Abholzung und Landnutzungsänderungen im Amazonas analysieren und modellieren lassen. Die Abholzung tropischer Wälder bedroht die Stabilität artenreicher Ökosysteme, lokaler Wettergeschehen und des globalen Klimas. Drei Hauptteile erforschen das Thema mit Konzepten der theoretischen Physik und Netzwerktheorie. Der erste Teil gibt einen kritischen Überblick über Modellansätze, die Entscheidungen und menschliches Verhalten beschreiben. Agentenbasierte Netzwerkmodelle ergeben sich als vielversprechender Ansatz um sozial-ökologische Systeme zu modellieren. Der zweite Teil identifiziert Muster in satellitengestützten Landbedeckungsdaten im brasilianischen Amazonas. Basierend auf der Theorie der Markov-Ketten werden Übergangsraten zwischen verschiedenen Typen von Landbedeckung berechnet und Übergangsmatrizen für Teilgebiete mit Clusteralgorithmen verglichen. Angrenzende Teilgebiete weisen ähnliche Übergänge auf. Die identifizierten Cluster decken sich mit Erkenntnissen aus Feldstudien. Auf Grundlage der geschätzten Übergangsrate ergeben sich Projektionen für die Entwicklung der Landbedeckungsanteile. Der dritte Teil entwickelt ein agentenbasiertes Modell um zu untersuchen, unter welchen Bedingungen die Intensivierung der Viehhaltung im Amazonas die Abholzung reduzieren kann. Das Modell kombiniert ökologische, ökonomische und soziale Prozesse und modelliert Landnutzungsstrategien mit Heuristiken. Die Modellanalyse zeigt, dass eine Intensivierung die Abholzung nur dann verringert, wenn der lokale Viehmarkt saturiert. Unter anderen ökonomischen Bedingungen kann Intensivierung die Abholzung erhöhen. Die Arbeit demonstriert, dass eine Kombination von Methoden aus der Theorie komplexer Systeme mit sozialwissenschaftlichen Theorien zu einem besseren Verständnis der emergenten Dynamik sozial-ökologischer Systeme führen kann – eine Grundvoraussetzung, um solche Systeme nachhaltig zu bewirtschaften.<br>This thesis investigates how to model and analyze human-nature interactions using the example of deforestation and land-use change in the Brazilian Amazon. Deforestation of tropical forests threatens the stability of species-rich ecosystems, local weather patterns, and global climate. The three main parts of the thesis study different aspects of this topic using concepts from theoretical physics and network theory. The first part reviews modeling approaches to human decision making and behavior. From the review, networked agent-based models emerge as promising tools to capture the dynamics of social-ecological systems such as the land system. The second part of the thesis combines Markov-chain and cluster analyses to detect patterns in satellite-derived land-cover maps of the Brazilian Amazon. I compute transition rates between different land-cover types and apply clustering algorithms to find spatial patterns. The analysis shows that neighboring subregions undergo similar transitions and identifies clusters corresponding to findings from field surveys. Markov-chain models, parameterized with the transition rates, are used to compute land-cover projections. In the third part, I develop an agent-based model to investigate under which conditions the intensification of cattle ranching can reduce deforestation in the Amazon. The model captures stylized environmental, economic, as well as social processes, and uses heuristic decision theory to represent different land management strategies. A detailed analysis reveals that fast intensification can only lower deforestation rates if local cattle markets saturate. Under other economic conditions intensification may increase deforestation. The contributions of this thesis demonstrate that combining modeling tools from complexity science with social-science theories allow better understanding the emergent dynamics of social-ecological systems, which is a prerequisite for their sustainable management.
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Palazzi, Nieves María José. "Structural and dynamical interdependencies in complex networks at meso- and macroscale: nestedness, modularity, and in-block nestedness." Doctoral thesis, Universitat Oberta de Catalunya, 2020. http://hdl.handle.net/10803/671886.
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Many real systems like the brain are considered to be complex, i.e. they are made of several interacting components and display a collective behaviour that cannot be inferred from how the individual parts behave. They are usually described as networks, with the components represented as nodes and the interactions between them as links. Research into networks mainly focuses on exploring how a network's dynamic behaviour is constrained by the nature and topology of the interactions between its elements. Analyses of this sort are performed on three scales: the microscale, based on single nodes; the macroscale, which explores the whole network; and the mesoscale, which studies groups of nodes. Nonetheless, most studies so far have focused on only one scale, despite increasing evidence suggesting that networks exhibit structure on several scales. In our thesis, we apply structural analysis to a variety of synthetic and empirical networks on multiple scales. We focus on the examination of nested, modular, and in-block nested patterns, and the effects that they impose on each other. Finally, we introduce a theoretical model to help us to better understand some of the mechanisms that enable such patterns to emerge.<br>Molts sistemes, com el cervell o internet, són considerats complexos: sistemes formats per una gran quantitat d'elements que interactuen entre si, que exhibeixen un comportament col·lectiu que no es pot inferir des de les propietats dels seus elements aïllats. Aquests sistemes s'estudien mitjançant xarxes, en les quals els elements constituents són els nodes, i les interaccions entre ells, els enllaços. La recerca en xarxes s'enfoca principalment a explorar com el comportament dinàmic d'una xarxa està definit per la naturalesa i la topologia de les interaccions entre els seus elements. Aquesta anàlisi sovint es fa en tres escales: la microescala, que estudia les propietats dels nodes individuals; la macroescala, que explora les propietats de tota la xarxa, i la mesoescala, basada en les propietats de grups de nodes. No obstant, la majoria dels estudis se centren només en una escala, tot i la creixent evidència que suggereix que les xarxes sovint exhibeixen estructura a múltiples escales. En aquesta tesi estudiarem les propietats estructurals de les xarxes a escala múltiple. Analitzarem les propietats estructurals dels patrons in-block nested i la seva relació amb els patrons niats i modulars. Finalment, introduirem un model teòric per explorar alguns dels mecanismes que permeten l'emergència d'aquests patrons.<br>Muchos sistemas, como el cerebro o internet, son considerados complejos: sistemas formados por una gran cantidad de elementos que interactúan entre sí, que exhiben un comportamiento colectivo que no puede inferirse desde las propiedades de sus elementos aislados. Estos sistemas se estudian mediante redes, en las que los elementos constituyentes son los nodos, y las interacciones entre ellos, los enlaces. La investigación en redes se enfoca principalmente a explorar cómo el comportamiento dinámico de una red está definido por la naturaleza y la topología de las interacciones entre sus elementos. Este análisis a menudo se hace en tres escalas: la microescala, que estudia las propiedades de los nodos individuales; la macroescala, que explora las propiedades de toda la red, y la mesoescala, basada en las propiedades de grupos de nodos. No obstante, la mayoría de los estudios se centran solo en una escala, a pesar de la creciente evidencia que sugiere que las redes a menudo exhiben estructura a múltiples escalas. En esta tesis estudiaremos las propiedades estructurales de las redes a escala múltiple. Analizaremos las propiedades estructurales de los patrones in-block nested y su relación con los patrones anidados y modulares. Finalmente, introduciremos un modelo teórico para explorar algunos de los mecanismos que permiten la emergencia de estos patrones.<br>Tecnologías de la información y de redes
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Silva, Juliana Saragiotto. "Métricas de análise de redes sociais e sua aplicação em redes de interação biológicas: metodologia de aplicação e estudos de caso." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-22052015-154651/.
Full textAbstract:
Diversos pesquisadores têm se utilizado do recurso de Redes de Interação na área de Biodiversidade para analisar o papel das espécies na estrutura da uma rede cujos fundamentos conceituais são os mesmos das Redes Sociais (como Facebook, LinkedIn, entre outras). Nesse sentido, algoritmos, métricas e recursos computacionais e estatísticos provenientes da área de Análise de Redes Sociais (Social Network Analysis SNA) são ferramentas importantes para endereçar/apoiar estudos com interações. Assim sendo, o objetivo desta tese é propor uma metodologia para aplicação das métricas de SNA em estudos com Redes de Interação biológicas no domínio da Informática para a Biodiversidade. A metodologia está formalizada por meio da Notação para Modelagem de Processos de Negócio (BPMN - Business Process Model and Notation) e estruturada em quatro etapas: (i) mapeamento dos tipos de dados e de interação disponíveis; (ii) definição das perguntas-chave a serem respondidas e das variáveis de análise; (iii) escolha das métricas de SNA adequadas ao contexto da pesquisa; e (iv) realização de análises biológicas com o apoio de SNA. Como recursos materiais foram utilizadas as métricas de SNA, bem como um conjunto de ferramentas computacionais (como os pacotes do R e os programas Dieta, Pajek e Ucinet) e de Análise Estatística (como a Análise Exploratória de Dados e a Análise Multivariada de Dados). Esta proposta nasceu de um processo de colaboração com pesquisadores de diversas áreas do conhecimento, a partir de projetos desenvolvidos no Núcleo de Pesquisa em Biodiversidade e Computação da USP (BioComp-USP), o que trouxe uma base de sustentação a esta metodologia. Para avaliar a adequação desta proposta a necessidades reais de pesquisa a metodologia foi aplicada a três estudos de caso com Redes de Interação microbiológicas. Os resultados mostram os benefícios que a disponibilização de um método sistematizado para guiar os passos de uma pesquisa pode trazer a um pesquisador seja em função do aporte de recursos recomendados, seja pelo processo de organização das atividades de pesquisa. Além disso, verifica-se a possibilidade de transposição desta proposta a outros domínios do conhecimento ainda não explorados, como em Agrobiodiversidade.<br>Several researchers have used Interaction Networks resources in the Biodiversity area for analyzing the role of species in network structure their conceptual foundations are the same as those in Social Networks (such as Facebook, LinkedIn, among others). Thus, algorithms, metrics, and statistical and computational resources from the Social Network Analysis (SNA) area are important tools for addressing this issue. Therefore, the aim of this thesis is to propose a methodology for applying SNA metrics to biological interaction network studies in the Biodiversity Informatics domain. The methodology is formalized by means of Business Process Model and Notation (BPMN) and structured in four steps: (i) mapping the data types and the interaction available; (ii) defining the key-questions to be answered and the analysis variable; (iii) choosing the SNA metrics appropriate to the context of the research; and (iv) performing the biological analysis with the support of SNA. As material resources, the SNA metrics were used, as well as a set of computational (such as R packages, Dieta, Pajek and Ucinet software) and Statistical Analysis (Exploratory Data Analysis and Multivariate Data Analysis) tools. This proposal generated a process collaboration with researchers from different knowledge areas, by means of projects developed at the Research Center on Biodiversity and Computing at USP (BioComp-USP), which provided a support base to this methodology. To assess the suitability of this proposal to the real research needs, it was applied to three case studies with microbiological Interaction Networks. The results show the benefits that providing a systematic method to guide the steps of one research can bring to a researcher be it due to the support of the resources recommended, be it by the organization of the research activities. In addition, there is the possibility of applying this methodology to unexplored knowledge fields, such as Agrobiodiversity.
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Campos, Ronaldo Ribeiro de. "Redes complexas e ações para compartilhamento de conhecimento: uma análise de redes sociais em um ambiente web para apoio à aprendizagem." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/18/18157/tde-24062014-105925/.
Full textAbstract:
A Gestão do Conhecimento pode ser definida como um conjunto de processos para coletar, armazenar, compartilhar e utilizar o conhecimento. No contexto deste trabalho, o processo de compartilhamento do conhecimento é aceito como um elemento fundamental para a realização da Gestão do Conhecimento, pois representa a interação entre os indivíduos que compõem uma rede de relacionamentos da qual o conhecimento emerge. A Análise de Redes Sociais (ARS) apresenta métricas que permitem identicar os relacionamentos da rede e analisálos, porém ainda existem necessidades de identificar ações que possam refletir em uma estrutura de rede que permita maiores possibilidades de compartilhamento do conhecimento. Neste trabalho, as técnicas da ARS foram aplicadas para analisar as características da estrutura de uma rede de estudantes formada a partir de um ambiente web representado pelo uso do Facebook©. A metodologia utilizada foi baseada em um estudo qualiquantitativo, classificado como uma pesquisa descritiva e exploratória. Foram analisados dezoito períodos semanais de comportamento da rede. As análises permitiram entender melhor a representatividade das métricas da ARS no contexto do compartilhamento do conhecimento e uma nova métrica foi proposta (degree-weight). Também foram propostas ações relacionadas às métricas. Um conjunto diferente de ações foi aplicado em duas outras redes. Os resultados indicaram diferentes comportamentos da rede para cada um dos conjuntos de ações. Foi possível ainda identificar maiores possibilidades de compartilhamento de conhecimento para uma das estruturas de rede.<br>Knowledge Management can be defined as a set of processes to capture, store, share and use knowledge. In the context of this work, the knowledge sharing process is accepted as a basic element to Knowledge Management because it represents the interaction among the individuals that compound a network of relationships from where knowledge emerges. The Social Network Analysis (SNA) offers metrics that make possible identify the network relationships and analyze them, but there still needs to identify actions that may reflect on a network structure that allows opportunities for knowledge sharing. The SNA techiniques were applied to anlyze the characteristics of a network compounded by students and created in a web environment which was represented by Facebook© . The methodology applied was based on a quantitative and qualitative study which was classified as a descriptive and exploratory research. Eigthteen periods of network behavior were analysed. The analyses allow us to understand better the representativeness of SNA metrics in the environment of knowledge sharing. A new metric called degree-weight was proposed. Also it was proposed a set of actions related to SNA metrics. A different set of actions was applied to two distinct networks. The results show us different network behaviors for each one of the set of actions. Also it was possible identify better conditions to the knowledge sharing process for one of the network structures.