Dissertations / Theses: 'Social Networks Analysis'

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Gamal, Doaa. "Social Networks Influence Analysis." UNF Digital Commons, 2017. http://digitalcommons.unf.edu/etd/723.

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Pew Research Center estimates that as of 2014, 74% of the Internet Users used social media, i.e., more than 2.4 billion users. With the growing popularity of social media where Internet users exchange their opinions on many things including their daily life encounters, it is not surprising that many organizations are interested in learning what users say about their products and services. To be able to play a proactive role in steering what user’s say, many organizations have engaged in efforts aiming at identifying efficient ways of marketing certain products and services, and making sure user reviews are somewhat favorable. Favorable reviews are typically achieved through identifying users on social networks who have a strong influence power over a large number of other users, i.e. influential users. Twitter has emerged as one of the prominent social network services with 320 million monthly active users worldwide. Based on the literature, influential Twitter users have been typically analyzed using the following three models: topic-based model, topology-based model, and user characteristics-based model. The topology-based model is criticized for being static, i.e., it does not adapt to the social network changes such as user’s new posts, or new relationships. The user characteristics-based model was presented as an alternative approach; however, it was criticized for discounting the impact of interactions between users, and users’ interests. Lastly, the topic-based model, while sensitive to users’ interests, typically suffers from ignoring the inclusion of inter-user interactions. This thesis research introduces a dynamic, comprehensive and topic-sensitive approach for identifying social network influencers leveraging the strengths of the aforementioned models. Three separate experiments were conducted to evaluate the new approach using the information diffusion measure. In these experiments, software was developed to capture users’ tweets pertinent to a topic over a period of time, and store the tweet’s metadata in a relational database. A graph representing users was extracted from the database. The new approach was applied to the users’ graph to compute an influence score for each user. Results show that the new composite influence score is more accurate in comprehensively identifying true influential users, when compared to scores calculated using the characteristics-based, topic-based, and topology-based models. Also, this research shows that the new approach could leverage a variety of machine learning algorithms to accurately identify influencers. Last, while the focus of this research was on Twitter, our approach may be applicable to other social networks and micro-blogging services.

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Junuthula, Ruthwik Reddy. "Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249.

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ATHANASIOU, THOMAS. "Multi-dimensional analysis of social multi-networks : Analysing a 5-layer social network case study." Thesis, Uppsala universitet, Institutionen för informatik och media, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-273908.

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Network theory analysis is applicable to many scientific disciplines (fields) such as biology, statistics and sociology. The social network analysis is one of the various branches of the broader network theory analysis, the social network analysis. It is of high interest among the researchers in social sciences. Social networks have had a significant impact on human civilizations for many centuries. During the last two decades, the main academic interest was addressed towards the research and analysis of a dynamically uprising sector of social networks, the on-line networks, primarily due to the domination of the Internet and technology over human attitudes and relations in modern societies. For many years, research was emphasized on the analysis of simple social networks, whilst during the last decade several researchers started working on the analysis of more complicated social networks, which consist by several smaller social networks. There are important differences between mono and multi-dimensional network analysis. Mono-dimensional analysis provides the research with relevant knowledge. On the other hand, multi-dimensional analysis is still at initial stage. As a result, several potential models related to the multi-networks analysis cannot always provide reliable and adequate outcomes. However, due to the fact that different social networks can be easily combined and form more extended and complicated networks, it is of high importance for the researchers to advance the multi-dimensional analysis and provide more adequate analytical models. The purpose of this thesis is to present the dynamic of the multi-dimensional analysis by consecutively applying both mono and multi-dimensional analysis on a social multi-network. The findings suggest that multi-dimensional analysis can add reliable knowledge on the social network analysis, but many problems that arose due the complexity of the multi-networks structures need to be addressed.

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Dang, The Anh. "Analysis of community in social networks." Paris 13, 2012. http://www.theses.fr/2012PA132043.

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Un réseau social est une structure composée d'entités reliées par un ou plusieurs types d'interdépendance, le plus souvent modélisé par un ou plusieurs graphes. Une caractéristique importante des réseaux sociaux est leur structure en communautés. Une communauté est définie comme un ensemble de nœuds qui interagissent d'avantage entre eux qu'avec le reste du réseau. Cette thèse porte sur l'analyse des communautés dans les réseaux sociaux, qui est utile pour de nombreuses tâches, telles la caractérisation de la structure, les systèmes de recommandation, la visualisation, ou encore le suivi de la dynamique. Nous proposons notamment des techniques pour découvrir les communautés dans les graphes bipartites, basé sur l'optimisation de modularités bipartites. Nous étudions ensuite la détection de communautés dans les graphes dont les nœuds sont associés à des attributs, comme cela est très souvent le cas dans les applications réelles. Nos algorithmes considèrent simultanément la structure et les attributs du graphe et détectent des communautés telles que les nœuds dans la même communauté soient densément connectés et portent des attributs proches. Les méthodes développées sont appliquées à l'analyse des communautés du site web social Skyrock et de réseaux de blogs, dans le cadre du projet ANR ExDEUSS CEDRES. Nous étudions aussi la contribution des informations extraites des communautés pour améliorer la performance des systèmes de recommandation. Enfin, nous proposons un modèle génératif de réseau social intégrant les attributs de nœuds et la structure des communautés, qui nous permet de proposer des jeux de tests artificiels simulant des réseaux complexes réels.

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Broccatelli, Chiara. "Going beyond secrecy : methodological advances for two-mode temporal criminal networks with Social Network Analysis." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/going-beyond-secrecy-methodological-advances-for-twomode-temporal-criminal-networks-with-social-network-analysis(f0f91f79-7bc3-442c-a16b-e9cf44cc68c3).html.

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This thesis seeks to extend the application of Social Network Analysis (SNA) to temporal graphs, in particular providing new insights for the understanding of covert networks. The analyses undertaken reveal informative features and properties of individuals' affiliations under covertness that also illustrate how both individuals and events influence the network structure. The review of the literature on covert networks provided in the initial two chapters suggests the presence of some ambiguities concerning how authors define structural properties and dynamics of covert networks. Authors sometimes disagree and use their findings to explain opposite views about covert networks. The controversy in the field is used as a starting point in order to justify the methodological application of SNA to understand how individuals involved in criminal and illegal activities interact with each other. I attempt to use a deductive approach, without preconceived notions about covert network characteristics. In particular, I avoid considering covert networks as organisations in themselves or as cohesive groups. I focus on individuals and their linkages constructed from their common participation in illicit events such as secret meetings, bombing attacks and criminal operations. In order to tackle these processes I developed innovative methods for investigating criminals' behaviours over time and their willingness to exchange tacit information. The strategy implies the formulation of a network model in order to represent and incorporate in a graph three types of information: individuals, events, and the temporal dimension of events. The inclusion of the temporal dimension offers the possibility of adopting a more comprehensive theoretical framework for considering individuals and event affiliations. This thesis expands the analysis of bipartite covert networks by adopting several avenues to explore in this perspective. Chapter 3 proposes a different way to represent two-mode networks starting from the use of line-graphs, namely the bi-dynamic line-graph data representation (BDLG), through which it is possible to represent the temporal evolution of individual's trajectories. The following chapter 4 presents some reflections about the idea of cohesion and cohesive subgroups specific to the case of two-mode networks. Based on the affiliation matrices, the analysis of local clustering through bi-cliques offers an attempt to analyse the mechanism of selecting accomplices while taking into account time. Chapter 5 is concerned with the concept of centrality of individuals involved in flows of knowledge exchanges. The theoretical and analytical framework helps in elaborating how individuals share their acquired hands-on experiences with others by attending joint task activities over time. Chapter 6 provides an application of the approaches introduced in the preceding chapters to the specific case of the Noordin Top terrorist network. Here, the knowledge of experience flow centrality measure opens up a new way to quantify the transmission of information and investigate the formation of the criminal capital. Finally, the last Chapter 7 presents some future research extensions by illustrating the versatility of the proposed approaches in order to provide new insights for the understanding of criminals' behaviours.

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Wang, Nan. "Modeling and analysis of massive social networks." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2683.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Lai, Ka Chon. "Constructing social networks based on image analysis." Thesis, University of Macau, 2012. http://umaclib3.umac.mo/record=b2586279.

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Bettaney, Elaine. "Analysis of association-derived animal social networks." Thesis, University of Bath, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629664.

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The social structure of animal societies can be instrumental to the evolution and maintenance of animal behaviour. Animal social networks (ASNs) provide a framework with which to visualise, quantify and analyse animals' social structure. The work in this thesis incorporates two areas of ASN research. The first area is the analysis of sparse group-derived data. Observation of group memberships is a widely used method to uncover social preferences. Here this method is used to probe the social structure of a population of Trinidadian guppies (Poecilia reticulata). The network is analysed to ascertain if genetic relatedness may play a role in governing social structure. The bright colourings of male fish are also analysed to see if colour influences male-male associations. The guppy study provided motivation for an investigation into association indices for group-derived data. Existing indices are evaluated using a simulated dataset and a new index is proposed. The second part of this thesis contributes to a new and exciting trend in ASNs in which complete records of animal associations are obtained enabling temporal network analysis to be used. This is applied to a population of New Caledonian crows (Corvus moneduloides) which are of interest particularly for their ability to manufacture and use tools for foraging. Emulations of information flow through the network are used to assess the network's information flow potential. A network structure in which information can spread rapidly could indicate that crows can potentially learn tool use skills from their peers.

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Magnusson, Jonathan. "Social Network Analysis Utilizing Big Data Technology." Thesis, Uppsala universitet, Avdelningen för datalogi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170926.

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As of late there has been an immense increase of data within modern society. This is evident within the field of telecommunications. The amount of mobile data is growing fast. For a telecommunication operator, this provides means of getting more information of specific subscribers. The applications of this are many, such as segmentation for marketing purposes or detection of churners, people about to switching operator. Thus the analysis and information extraction is of great value. An approach of this analysis is that of social network analysis. Utilizing such methods yields ways of finding the importance of each individual subscriber in the network. This thesis aims at investigating the usefulness of social network analysis in telecommunication networks. As these networks can be very large the methods used to study them must scale linearly when the network size increases. Thus, an integral part of the study is to determine which social network analysis algorithms that have this scalability. Moreover, comparisons of software solutions are performed to find product suitable for these specific tasks. Another important part of using social network analysis is to be able to interpret the results. This can be cumbersome without expert knowledge. For that reason, a complete process flow for finding influential subscribers in a telecommunication network has been developed. The flow uses input easily available to the telecommunication operator. In addition to using social network analysis, machine learning is employed to uncover what behavior is associated with influence and pinpointing subscribers behaving accordingly.

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Hu, Wei Shu. "Community detection and credibility analysis on social networks." Thesis, University of Macau, 2015. http://umaclib3.umac.mo/record=b3335428.

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Coletto, Mauro. "Analysis of Polarized Communities in Online Social Networks." Thesis, IMT Alti Studi Lucca, 2017. http://e-theses.imtlucca.it/204/1/Coletto_phdthesis.pdf.

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Increasingly, people around the globe use Social Media (SM) - e.g. Facebook, Twitter, Tumblr, Flickr, Youtube - to publish multimedia content (posting), to share it (retweeting, reblogging or resharing), to reinforce it or not (liking, disliking, favoriting) and to discuss (through messages and comments) in order to be in contact with other users and to get informed about topics of interest. The world population is ≈ 7:4 billion people, among them ≈ 2:3 billion (31%) are active social media users (GlobalWeb Index data, Jan 2016). In fact, these virtual contexts answer the human need of aggregation that nowadays is translated into digital bonds among peers all over the world, in addition to the traditional face-to-face relationships. Online Social Networks (OSNs), then, provide a space for user aggregation in groups, expressing opinions, accessing information, contributing to public debates, and participating in the formation of belief systems. In this context, communities are built around different topics of interaction and polarized sub-groups often emerge by clustering different opinions and points of view. Such polarized sub-groups can be tracked and monitored over time in an automatic way and the analysis of their interactions is interesting to shed light on the human social behavior. Even though many studies have been devoted to understand different aspects of the social network structure and its function, such as, community structure (For10), information spreading (BRMA12), information seeking (KLPM10), link prediction (LNK07), etc., much less work is available on analyzing online discussions, user opinion and public debates. In this doctoral dissertation we analyze the concept of polarization by looking at interactions among users in different Online Social Networks. Polarization is a social process whereby a social group is divided into sub-communities discussing different topics and having different opinions, goals and viewpoints, often conflicting and contrasting (Sun02; Ise86). We are interested in studying how and to what extend it is possible to extract information about polarized communities by automatically processing the data about interactions created in Online Social Networks. We present the state of the art and we propose a novel detecting method which allows to identify polarized groups, track them and monitor the topic evolution in the discussion among users of an OSN over time by classifing the keywords used in the messages exchanged. We show that it improves the state of the art and we describe case studies conducted particularly on Twitter (CLOP16; CGGL17). The benefits in understanding user opinions are detailed in the first chapters. Moreover, we use the proposed methodology and alternatives in different application contexts: misinformation (BCD+14a; BCD+14b; BCD+15), politics (CLOP16; CLOP15; CLO+15), social behaviors (CALS16a; CALS16b), and migrations (CLM+16). A further application of opinion mining is the task of predicting user behavior. We discuss the limitations and the challenges related to this research area by looking at the context of political elections and by digging into a case study of electoral prediction. We believe that the analysis of polarized communities is OSNs can be used to predict collective social behavior, but major improvements in the field can be achieved by integrating several sources of information, such as traditional surveys, multiple Online Social Networks, demographic data, historical information, events, cyber-physical data. Therefore, polarization is integrated in a framework of analysis with other dimensions (time, location) to explore social phenomena from a social media perspective. In particular, we look at the possibility to understand European perception of the political refugees’ crises by mining OSN data. The concept of polarization is related to that of controversy. Controversy describes the interaction among two or more opponent polarized communities that discuss together, often with heated tones. For some highly controversial topics (e.g., politics, religion, ethics) even though users prefer to get informed though polarized content originated in the communities they belong to, they like to share their affiliations, believes, ideals, convictions with external users in order to persuade them in joining their belief system or supporting, criticizing an event, a group, a party or a specific person. Highly polarization does not always imply controversy and vice versa. We describe the recent literature about controversy detection and we propose a machine learning approach which takes into account features related to the social network and to conversational interaction patterns. The model is able to identify controversy in a conversation without any feature related to the content of the interaction. The features are deeply analyzed and the accuracy of the model is discussed. We finally explore two opposite situations. The first is the formation of echo chambers, where a user gets informed and gives opinions in a self-contained group, whose members share a similar point of view. By analyzing communities in Facebook which consume news from scientific pages and from pages focused on conspiracy theories we confirm the hypothesis of cognitive closure of the users, weakening the idea of Social Media as a space for democratic collective intelligence. The second is the presence of deviant communities. Those are communities that emerge around what are usually referred to as deviant behaviors (CM15), conducts that are commonly considered inappropriate because they violate society’s norms or moral standards. An example of deviant behavior is the pornography consumption, that is the focus of our examination looking at content dissemination in Online Social Networks. Deviant communities are commonly considered segregated but we show that instead their content might spread far away in the Online Social Network. We analyze both situations with real case studies using Facebook, Flickr, and Tumblr data. Our work is an initial study of opinion polarization on Online Social Networks with some in-depth analyses of specific topical user communities. It brings novel contributions in: i) characterizing communities through the perspective of user polarization; ii) proposing a novel method to classify polarized users and topic evolution over time; iii) understanding user behavior from a social media perspective; iv) integrating polarization with other variables (time, space) with the purpose of analyzing a social phenomenon; v) defining controversy and how to detect it regardless of the content; vi) describing how people aggregate and share information in various contexts. Different topical communities and several OSNs are described in the dissertation, providing a general overview of the investigation field and proposing contributions to the discussion and solutions. Our research questions are part of a broader research area which is called Computational Social Science. This new discipline - which is the frame of our thesis - is a new approach to social studies by mean of novel large-scale computational tools, merging Social Science with Computer Science and Machine Learning.

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Munasib, Abdul Baten Ahmed. "Lifecycle of social networks a dynamic analysis of social capital accumulation /." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1121441394.

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Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xiv, 130 p.; also includes graphics. Includes bibliographical references (p. 121-130). Available online via OhioLINK's ETD Center

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Pister, Alexis. "Visual Analytics for Historical Social Networks : Traceability, Exploration, and Analysis." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG081.

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Cette thèse vise à identifier théoriquement et concrètement comment l'analyse visuelle peut aider les historiens dans leur processus d'analyse de réseaux sociaux. L'analyse de réseaux sociaux est une méthode utilisée en histoire sociale qui vise à étudier les relations sociales au sein de groupes d'acteurs (familles, institutions, entreprises, etc.) en reconstruisant les relations du passé à partir de documents historiques, tels que des actes de mariages, des actes de naissances, ou des recensements. L'utilisation de méthodes visuelles et analytiques leurs permet d'explorer la structure sociale formant ces groupes et de relier des mesures structurelles à des hypothèses sociologiques et des comportements individuels. Cependant, l'inspection, l'encodage et la modélisation des sources menant à un réseau finalisé donnent souvent lieu à des erreurs, des distorsions et des problèmes de traçabilité, et les systèmes de visualisation actuels présentent souvent des défauts d'utilisabilité et d'interprétabilité. En conséquence, les historiens ne sont pas toujours en mesure de faire des conclusions approfondies à partir de ces systèmes : beaucoup d'études se limitent à une description qualitative d'images de réseaux, surlignant la présence de motifs d'intérêts (cliques, îlots, ponts, etc.). Le but de cette thèse est donc de proposer des outils d'analyse visuelle adaptés aux historiens afin de leur permettre une meilleur intégration de leur processus global et des capacités d'analyse guidées. En collaboration avec des historiens, je formalise le processus d'une analyse de réseau historique, de l'acquisition des sources jusqu'à l'analyse finale, en posant comme critère que les outils utilisés dans ce processus devraient satisfaire des principes de traçabilité, de simplicité et de réalité documentaire (i.e., que les données présentées doivent être conformes aux sources) pour faciliter les va-et-vient entre les différentes étapes et la prise en main par l'utilisateur et ne pas distordre le contenu des sources. Pour satisfaire ces propriétés, je propose de modéliser les sources historiques en réseaux sociaux bipartis multivariés dynamiques avec rôles. Ce modèle intègre explicitement les documents historiques sous forme de nœuds, ce qui permet aux utilisateurs d'encoder, de corriger et d'analyser leurs données avec les mêmes outils. Je propose ensuite deux interfaces d'analyse visuelle permettant, avec une bonne utilisabilité et interprétabilité, de manipuler, d'explorer et d'analyser ce modèle de données. Le premier système ComBiNet offre une exploration visuelle de l'ensemble des dimensions du réseau à l'aide de vues coordonnées et d'un système de requêtes visuelles permettant d'isoler des individus ou des groupes et de comparer leurs structures topologiques et leurs propriétés. L'outil permet également de détecter les motifs inhabituels et ainsi de déceler les éventuelles erreurs dans les annotations. Le second système, PK-Clustering, est une proposition d'amélioration de l'utilisabilité et de l'efficacité des mécanismes de clustering dans les systèmes de visualisation de réseaux sociaux. L'interface permet de créer des regroupements pertinents à partir des connaissances a priori de l'utilisateur, du consensus algorithmique et de l'exploration du réseau dans un cadre d'initiative mixte. Les deux systèmes ont été conçus à partir des besoins et retours continus d'historiens, et visent à augmenter la traçabilité, la simplicité, et la réalité documentaire des sources dans le processus d'analyse de réseaux historiques. Je conclus sur la nécessité d'une meilleure intégration des systèmes d'analyse visuelle dans le processus de recherche des historiens. Cette intégration nécessite des outils plaçant les utilisateurs au centre du processus avec un accent sur la flexibilité et l'utilisabilité, limitant ainsi l'introduction de biais et les barrières d'utilisation des méthodes quantitatives, qui subsistent en histoire
This thesis aims at identifying theoretically and concretely how visual analytics can support historians in their social network analysis process. Historical social network analysis is a method to study social relationships between groups of actors (families, institutions, companies, etc.) through a reconstruction of relationships of the past from historical documents, such as marriage acts, migration forms, birth certificates, and censuses. The use of visualization and analytical methods lets social historians explore and describe the social structure shaping those groups while explaining sociological phenomena and individual behaviors through computed network measures. However, the inspection and encoding of the sources leading to a finalized network is intricate and often results in inconsistencies, errors, distortions, and traceability problems, and current visualization tools typically have usability and interpretability issues. For these reasons, social historians are not always able to make thorough historical conclusions: many studies consist of qualitative descriptions of network drawings highlighting the presence of motifs such as cliques, components, bridges, etc. The goal of this thesis is therefore to propose visual analytics tools integrated into the global social historians' workflow, with guided and easy-to-use analysis capabilities. From collaborations with historians, I formalize the workflow of historical network analysis starting at the acquisition of sources to the final visual analysis. By highlighting recurring pitfalls, I point out that tools supporting this process should satisfy traceability, simplicity, and document reality principles to ease bask and forth between the different steps, provide tools easy to manipulate, and not distort the content of sources with modifications and simplifications. To satisfy those properties, I propose to model historical sources into bipartite multivariate dynamic social networks with roles as they provide a good tradeoff of simplicity and expressiveness while modeling explicitly the documents, hence letting users encode, correct, and analyze their data with the same abstraction and tools. I then propose two interactive visual interfaces to manipulate, explore, and analyze this data model, with a focus on usability and interpretability. The first system ComBiNet allows an interactive exploration leveraging the structure, time, localization, and attributes of the data model with the help of coordinated views and a visual query system allowing users to isolate interesting groups and individuals, and compare their position, structures, and properties. It also lets them highlight erroneous and inconsistent annotations directly in the interface. The second system, PK-Clustering, is a concrete proposition to enhance the usability and effectiveness of clustering mechanisms in social network visual analytics systems. It consists in a mixed-initiative clustering interface that let social scientists create meaningful clusters with the help of their prior knowledge, algorithmic consensus, and interactive exploration of the network. Both systems have been designed with continuous feedback from social historians, and aim to increase the traceability, simplicity, and document reality of visual analytics supported historical social network research. I conclude with discussions on the potential merging of both tools, and more globally on research directions towards better integration of visual analytics systems on the whole workflow of social historians. Systems with a focus on those properties---traceability, simplicity, and document reality---can limit the introduction of bias while lowering the requirements for the use of quantitative methods for historians and social scientists which has always been a controversial discussion among practitioners

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Zhang, Huiqi. "Socioscope: Human Relationship and Behavior Analysis in Mobile Social Networks." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc30533/.

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The widely used mobile phone, as well as its related technologies had opened opportunities for a complete change on how people interact and build relationship across geographic and time considerations. The convenience of instant communication by mobile phones that broke the barrier of space and time is evidently the key motivational point on why such technologies so important in people's life and daily activities. Mobile phones have become the most popular communication tools. Mobile phone technology is apparently changing our relationship to each other in our work and lives. The impact of new technologies on people's lives in social spaces gives us the chance to rethink the possibilities of technologies in social interaction. Accordingly, mobile phones are basically changing social relations in ways that are intricate to measure with any precision. In this dissertation I propose a socioscope model for social network, relationship and human behavior analysis based on mobile phone call detail records. Because of the diversities and complexities of human social behavior, one technique cannot detect different features of human social behaviors. Therefore I use multiple probability and statistical methods for quantifying social groups, relationships and communication patterns, for predicting social tie strengths and for detecting human behavior changes and unusual consumption events. I propose a new reciprocity index to measure the level of reciprocity between users and their communication partners. The experimental results show that this approach is effective. Among other applications, this work is useful for homeland security, detection of unwanted calls (e.g., spam), telecommunication presence, and marketing. In my future work I plan to analyze and study the social network dynamics and evolution.

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Fidalgo, Patrícia Seferlis Pereira. "Learning networks and moodle use in online courses: a social network analysis study." Doctoral thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8862.

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Dissertação para obtenção do Grau de Doutor em Ciências da Educação Especialidade em Tecnologias, Redes e Multimédia na Educação e Formação
This research presents a case study on the interactions between the participants of the forums of four online undergraduate courses from the perspective of social network analysis (SNA). Due to lack of studies on social networks in online learning environments in higher education in Portugal we have choose a qualitative structural analysis to address this phenomenon. The context of this work was given by the new experiences in distance education (DE) that many institutions have been making. Those experiences are a function of the changes in educational paradigms and due to a wider adoption of Information and Communication Technologies (ICT) from schools as well as to the competitive market. Among the technologies adopted by universities are the Learning Management Systems (LMSs) that allow recording, storing and using large amounts of relational data about their users and that can be accessed through Webtracking. We have used this information to construct matrices that allowed the SNA. In order to deepen knowledge about the four online courses we were studying we have also collect data with questionnaires and interviews and we did a content analysis to the participations in the forums. The three main sources of data collection led us to three types of analysis: SNA, statistical analysis and content analysis. These types of analysis allowed, in turn, a three-dimensional study on the use of the LMS: 1) the relational dimension through the study of forums networks and patterns of interaction among participants in those networks, 2) the dimension relative to the process of teaching and learning through content analysis of the interviews; 3) and finally the dimension related to the participants' perceptions about the use of LMS for educational purposes and as a platform for creating social networks through the analysis of questionnaires.With the results obtained we carried out a comparative study between the four courses and tried to present a reflection on the Online Project of the University as well as possible causes that led to what was observed. We have finished with a proposal of a framework for studying the relational aspects of online learning networks aimed at possible future research in this area.

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Trier, Matthias. "Towards a Social Network Intelligence Tool for visual Analysis of Virtual Communication Networks." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-140161.

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Communities of Practice regularly utilize virtual means of communication. The according software support provides its members with many sophisticated features for generating content and for communicating with each other via the internet or intranet. However, functionalities to monitor, assess, coordinate, and communicate the quality and development of the underlying electronic networks of experts are frequently missing. To meet this need of increased manageability, this contribution introduces a Social Network Intelligence software approach which aims at supporting the comprehension of the structure and value of electronic communities by automatically extracting and mining available electronic data of various types of virtual communication networks, like e-mail archives, discussion groups, or instant messaging communication. Experimental structural visualizations employing Social Network Analysis methods are combined with Keyword Extraction to move towards a Social Network Intelligence approach which generates transparency of complex virtual communication networks. Together with a comprehensive visualization method, an approach for software-supported communication network measurement and evaluation is suggested. It supports the identification of important participants, topics, or clusters in the network, evaluates the interpersonal communication structure and visually traces the evolvement of the knowledge exchange over time.

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Trier, Matthias. "Towards a Social Network Intelligence Tool for visual Analysis of Virtual Communication Networks." Technische Universität Dresden, 2006. https://tud.qucosa.de/id/qucosa%3A27871.

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Communities of Practice regularly utilize virtual means of communication. The according software support provides its members with many sophisticated features for generating content and for communicating with each other via the internet or intranet. However, functionalities to monitor, assess, coordinate, and communicate the quality and development of the underlying electronic networks of experts are frequently missing. To meet this need of increased manageability, this contribution introduces a Social Network Intelligence software approach which aims at supporting the comprehension of the structure and value of electronic communities by automatically extracting and mining available electronic data of various types of virtual communication networks, like e-mail archives, discussion groups, or instant messaging communication. Experimental structural visualizations employing Social Network Analysis methods are combined with Keyword Extraction to move towards a Social Network Intelligence approach which generates transparency of complex virtual communication networks. Together with a comprehensive visualization method, an approach for software-supported communication network measurement and evaluation is suggested. It supports the identification of important participants, topics, or clusters in the network, evaluates the interpersonal communication structure and visually traces the evolvement of the knowledge exchange over time.

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Cimenler, Oguz. "Social Network Analysis of Researchers' Communication and Collaborative Networks Using Self-reported Data." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5201.

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This research seeks an answer to the following question: what is the relationship between the structure of researchers' communication network and the structure of their collaborative output networks (e.g. co-authored publications, joint grant proposals, and joint patent applications), and the impact of these structures on their citation performance and the volume of collaborative research outputs? Three complementary studies are performed to answer this main question as discussed below. 1. Study I: A frequently used output to measure scientific (or research) collaboration is co-authorship in scholarly publications. Less frequently used are joint grant proposals and patents. Many scholars believe that co-authorship as the sole measure of research collaboration is insufficient because collaboration between researchers might not result in co-authorship. Collaborations involve informal communication (i.e., conversational exchange) between researchers. Using self-reports from 100 tenured/tenure-track faculty in the College of Engineering at the University of South Florida, researchers' networks are constructed from their communication relations and collaborations in three areas: joint publications, joint grant proposals, and joint patents. The data collection: 1) provides a rich data set of both researchers' in-progress and completed collaborative outputs, 2) yields a rating from the researchers on the importance of a tie to them 3) obtains multiple types of ties between researchers allowing for the comparison of their multiple networks. Exponential Random Graph Model (ERGM) results show that the more communication researchers have the more likely they produce collaborative outputs. Furthermore, the impact of four demographic attributes: gender, race, department affiliation, and spatial proximity on collaborative output relations is tested. The results indicate that grant proposals are submitted with mixed gender teams in the college of engineering. Besides, the same race researchers are more likely to publish together. The demographics do not have an additional leverage on joint patents. 2. Study II: Previous research shows that researchers' social network metrics obtained from a collaborative output network (e.g., joint publications or co-authorship network) impact their performance determined by g-index. This study uses a richer dataset to show that a scholar's performance should be considered with respect to position in multiple networks. Previous research using only the network of researchers' joint publications shows that a researcher's distinct connections to other researchers (i.e., degree centrality), a researcher's number of repeated collaborative outputs (i.e., average tie strength), and a researchers' redundant connections to a group of researchers who are themselves well-connected (i.e., efficiency coefficient) has a positive impact on the researchers' performance, while a researcher's tendency to connect with other researchers who are themselves well-connected (i.e., eigenvector centrality) had a negative impact on the researchers' performance. The findings of this study are similar except that eigenvector centrality has a positive impact on the performance of scholars. Moreover, the results demonstrate that a researcher's tendency towards dense local neighborhoods (as measured by the local clustering coefficient) and the researchers' demographic attributes such as gender should also be considered when investigating the impact of the social network metrics on the performance of researchers. 3. Study III: This study investigates to what extent researchers' interactions in the early stage of their collaborative network activities impact the number of collaborative outputs produced (e.g., joint publications, joint grant proposals, and joint patents). Path models using the Partial Least Squares (PLS) method are run to test the extent to which researchers' individual innovativeness, as determined by the specific indicators obtained from their interactions in the early stage of their collaborative network activities, impacts the number of collaborative outputs they produced taking into account the tie strength of a researcher to other conversational partners (TS). Within a college of engineering, it is found that researchers' individual innovativeness positively impacts the volume of their collaborative outputs. It is observed that TS positively impacts researchers' individual innovativeness, whereas TS negatively impacts researchers' volume of collaborative outputs. Furthermore, TS negatively impacts the relationship between researchers' individual innovativeness and the volume of their collaborative outputs, which is consistent with `Strength of Weak Ties' Theory. The results of this study contribute to the literature regarding the transformation of tacit knowledge into explicit knowledge in a university context.

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Franco, Alessia <1996&gt. "How Blockchain Technology Can Help Rearchitect Social Networks: An Analysis of Desmos Network." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19800.

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Centralized Social Networks (CSNs) such as Facebook, Instagram and Twitter are operated in an obscure, opaque and autocratic manner; many problems arise from centralized corporate power, these issues are based on the misalignment between profit-seeking incentives of corporations and user goals. Censorship, banning and data breaches have become increasingly frequent in the past five years, recent political tensions and the outbreak of Covid-19 have worsened this situation globally. The main issue has an inborn nature: the business model of CSNs utilizes and monetizes users’ data to maximize the profitability of tech giants. In this thesis I explore the case of Desmos, which proposes an alternative approach to social media creation and management. Based on the belief that that the root cause of this problem is the broken relationship between the different parties, Desmos offers a project to fix this broken relationship using blockchain technology. Through a redesigned lifecycle and an improved economic model it can achieve the network growth required for a self-sustainable social network, without the need for centralized intermediaries, thus prioritizing the interests of users. Desmos is therefore the protocol that can be used to build decentralized social networks, while Desmos Token (DSM) is the native token of this blockchain, which allows the holders to contribute to the security and governance of the platform in exchange for incentives.

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Chiu, Wei-Yi. "The analysis of social capital in online social communities." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/46995/1/Wei-Yi_Chiu_Thesis.pdf.

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Social networks have proven to be an attractive avenue of investigation for researchers since humans are social creatures. Numerous literature have explored the term “social networks” from different perspectives and in diverse research fields. With the popularity of the Internet, social networking has taken on a new dimension. Online social communities therefore have become an emerging social avenue for people to communicate in today’s information age. People use online social communities to share their interests, maintain friendships, and extend their so-called circle of “friends”. Likewise, social capital, also known as human capital, is an important theory in sociology. Researchers usually utilise social capital theory when they investigate the topic relating to social networks. However, there is little literature that can provide an explicit and strong assertion in that research area due to the complexity of social capital. This thesis therefore focuses on the issue related to providing a better understanding about the relationship between social capital and online social communities. To enhance the value within the scope of this analysis, an online survey was conducted to examine the effects of the dimensions of social capital: relational capital, structural capital, and cognitive capital, determining the intensity of using online social communities. The data were derived from a total of 350 self-selected respondents completing an online survey during the research period. The main results indicate that social capital exists in online social communities under normal circumstances. Finally, this thesis also presents three contributions for both theory and practice in Chapter 5. The main results contribute to the understanding of connectivity in the interrelationships between individual social capital exchange within online social networks. Secondly, social trust was found to have a weak effect in influencing the intensity of individuals using online social communities. Third, the perpetual role of information sharing has an indirect influence on individual users participating in online social communities. This study also benefits online marketing consultants as marketers can not only gain consumer information easier from online social communities but also this understanding assists in designing effective communication within online social communities. The cross-sectional study, the reliability of Internet survey data, and sampling issues are the major three limitations in this research. The thesis provides a new research model and recommends that the mediating effects, privacy paradox, and social trust on online social communities should be further explored in future research.

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McGlohon, Mary. "Structural Analysis of Large Networks: Observations and Applications." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/18.

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Network data (also referred to as relational data, social network data, real graph data) has become ubiquitous, and understanding patterns in this data has become an important research problem. We investigate how interactions in social networks are formed and how these interactions facilitate diffusion, model these behaviors, and apply these findings to real-world problems. We examined graphs of size up to 16 million nodes, across many domains from academic citation networks, to campaign contributions and actor-movie networks. We also performed several case studies in online social networks such as blogs and message board communities. Our major contributions are the following: (a) We discover several surprising patterns in network topology and interactions, such as Popularity Decay power law (in-links to a blog post decay with a power law with -1:5 exponent) and the oscillating size of connected components; (b) We propose generators such as the Butterfly generator that reproduce both established and new properties found in real networks; (c) several case studies, including a proposed method of detecting misstatements in accounting data, where using network effects gave a significant boost in detection accuracy.

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Moore, John David. "Making Sense of Networks: Exploring How Network Participants Understand and Use Information From Social Network Analysis." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/103640.

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Many of today's complex public issues are best addressed by multi-sectoral multi-organizational responses that include different types of organizations working together (Kettl, 2008; O'Toole, 1997). Social network analysis (SNA) of interorganizational networks has emerged as a useful tool for network managers to understand the structure and function of the complex networks in which they seek to manage (Human and Provan, 1997, 2000; Provan and Milward, 1995; Provan, Sebastian, and Milward, 1996; Provan, Veazie, Staten, and Teufel-Shone, 2005). The output of an interorganizational SNA typically provides a range of information to network managers including network plots. The network plots provide visual representations of different aspects of the network by showing the kinds of ties between the actors in the network. The information from network analyses can help network managers encourage systems thinking, see the different roles played by organizations, or identify links to outside resources among many other uses, but "will only have practical value to communities if it can be effectively presented, discussed, accepted, and acted on by community leaders and network participants [emphasis added]." (Provan et al., 2005, p. 610). However, little is currently known about if or how the information embedded in network plots is accepted or acted on by network participants. The visual representations of the network (network plots) provided to network participants following a SNA are often open to a range of interpretations that may or may not align with the findings of the analyst or the intended use by network managers, raising many interesting questions. Little is currently known about how differently situated network participants might interpret the same network plots differently. Nor do we understand what factors might influence different individuals or organizations to come up with different interpretations. After conducting a SNA and presenting it to network participants, I conducted interviews with a range of different representatives from participating organizations. I used a particular form of semi-structured interview, a situated micro-element interview from Dervin's Sense Making Methodology (SMM) (Dervin, Foreman-Wernet, and Lauterbach, 2003). I then analyzed the interview transcripts using standard qualitative coding methods (Bailey, 2007) to see if themes emerged that addressed the research questions. I found that most informants had trouble extracting information and meaning from their examination of the plots without that meaning and interpretation being provided by the expert analyst. I posit some potential explanations for why that might be so in the case I studied. I then turn to some interesting methodological considerations that emerged from taking the perspectives of network participants seriously. Finally, I synthesize the subject area and methodological findings into a refined framework for sense-making around network plots and offer propositions and potential approaches for future research.
Doctor of Philosophy
Many of today's complex public issues are best addressed by multi-sectoral multi-organizational responses that include different types of organizations working together (Kettl, 2008; O'Toole, 1997). Social network analysis (SNA) of interorganizational networks has emerged as a useful tool for network managers to understand the structure and function of the complex networks in which they seek to manage (Human and Provan, 1997, 2000; Provan and Milward, 1995; Provan, Sebastian, and Milward, 1996; Provan, Veazie, Staten, and Teufel-Shone, 2005). The output of an interorganizational SNA typically provides a range of information to network managers including network plots. The network plots provide visual representations of different aspects of the network by showing the kinds of ties between the actors in the network. The information from network analyses can help network managers encourage systems thinking, see the different roles played by organizations, or identify links to outside resources among many other uses, but "will only have practical value to communities if it can be effectively presented, discussed, accepted, and acted on by community leaders and network participants [emphasis added]." (Provan et al., 2005, p. 610). However, little is currently known about if or how the information embedded in network plots is accepted or acted on by network participants. The visual representations of the network (network plots) provided to network participants following a SNA are often open to a range of interpretations that may or may not align with the findings of the analyst or the intended use by network managers, raising many interesting questions. Little is currently known about how differently situated network participants might interpret the same network plots differently. Nor do we understand what factors might influence different individuals or organizations to come up with different interpretations. After conducting a SNA and presenting it to network participants, I conducted interviews with a range of different representatives from participating organizations. I used a particular form of semi-structured interview, a situated micro-element interview from Dervin's Sense Making Methodology (SMM) (Dervin, Foreman-Wernet, and Lauterbach, 2003). I then analyzed the interview transcripts using standard qualitative coding methods (Bailey, 2007) to see if themes emerged that addressed the research questions. I found that most informants had trouble extracting information and meaning from their examination of the plots without that meaning and interpretation being provided by the expert analyst. I posit some potential explanations for why that might be so in the case I studied. I then turn to some interesting methodological considerations that emerged from taking the perspectives of network participants seriously. Finally, I synthesize the subject area and methodological findings into a refined framework for sense-making around network plots and offer propositions and potential approaches for future research.

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23

Moore, John. "Making Sense of Networks: Exploring How Network Participants Understand and Use Information From Social Network Analysis." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103640.

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Many of today's complex public issues are best addressed by multi-sectoral multi-organizational responses that include different types of organizations working together (Kettl, 2008; O'Toole, 1997). Social network analysis (SNA) of interorganizational networks has emerged as a useful tool for network managers to understand the structure and function of the complex networks in which they seek to manage (Human and Provan, 1997, 2000; Provan and Milward, 1995; Provan, Sebastian, and Milward, 1996; Provan, Veazie, Staten, and Teufel-Shone, 2005). The output of an interorganizational SNA typically provides a range of information to network managers including network plots. The network plots provide visual representations of different aspects of the network by showing the kinds of ties between the actors in the network. The information from network analyses can help network managers encourage systems thinking, see the different roles played by organizations, or identify links to outside resources among many other uses, but "will only have practical value to communities if it can be effectively presented, discussed, accepted, and acted on by community leaders and network participants [emphasis added]." (Provan et al., 2005, p. 610). However, little is currently known about if or how the information embedded in network plots is accepted or acted on by network participants. The visual representations of the network (network plots) provided to network participants following a SNA are often open to a range of interpretations that may or may not align with the findings of the analyst or the intended use by network managers, raising many interesting questions. Little is currently known about how differently situated network participants might interpret the same network plots differently. Nor do we understand what factors might influence different individuals or organizations to come up with different interpretations. After conducting a SNA and presenting it to network participants, I conducted interviews with a range of different representatives from participating organizations. I used a particular form of semi-structured interview, a situated micro-element interview from Dervin's Sense Making Methodology (SMM) (Dervin, Foreman-Wernet, and Lauterbach, 2003). I then analyzed the interview transcripts using standard qualitative coding methods (Bailey, 2007) to see if themes emerged that addressed the research questions. I found that most informants had trouble extracting information and meaning from their examination of the plots without that meaning and interpretation being provided by the expert analyst. I posit some potential explanations for why that might be so in the case I studied. I then turn to some interesting methodological considerations that emerged from taking the perspectives of network participants seriously. Finally, I synthesize the subject area and methodological findings into a refined framework for sense-making around network plots and offer propositions and potential approaches for future research.
Doctor of Philosophy
Many of today's complex public issues are best addressed by multi-sectoral multi-organizational responses that include different types of organizations working together (Kettl, 2008; O'Toole, 1997). Social network analysis (SNA) of interorganizational networks has emerged as a useful tool for network managers to understand the structure and function of the complex networks in which they seek to manage (Human and Provan, 1997, 2000; Provan and Milward, 1995; Provan, Sebastian, and Milward, 1996; Provan, Veazie, Staten, and Teufel-Shone, 2005). The output of an interorganizational SNA typically provides a range of information to network managers including network plots. The network plots provide visual representations of different aspects of the network by showing the kinds of ties between the actors in the network. The information from network analyses can help network managers encourage systems thinking, see the different roles played by organizations, or identify links to outside resources among many other uses, but "will only have practical value to communities if it can be effectively presented, discussed, accepted, and acted on by community leaders and network participants [emphasis added]." (Provan et al., 2005, p. 610). However, little is currently known about if or how the information embedded in network plots is accepted or acted on by network participants. The visual representations of the network (network plots) provided to network participants following a SNA are often open to a range of interpretations that may or may not align with the findings of the analyst or the intended use by network managers, raising many interesting questions. Little is currently known about how differently situated network participants might interpret the same network plots differently. Nor do we understand what factors might influence different individuals or organizations to come up with different interpretations. After conducting a SNA and presenting it to network participants, I conducted interviews with a range of different representatives from participating organizations. I used a particular form of semi-structured interview, a situated micro-element interview from Dervin's Sense Making Methodology (SMM) (Dervin, Foreman-Wernet, and Lauterbach, 2003). I then analyzed the interview transcripts using standard qualitative coding methods (Bailey, 2007) to see if themes emerged that addressed the research questions. I found that most informants had trouble extracting information and meaning from their examination of the plots without that meaning and interpretation being provided by the expert analyst. I posit some potential explanations for why that might be so in the case I studied. I then turn to some interesting methodological considerations that emerged from taking the perspectives of network participants seriously. Finally, I synthesize the subject area and methodological findings into a refined framework for sense-making around network plots and offer propositions and potential approaches for future research.

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Reed, Markum L. "An Empirical Approach to Social Networks." OpenSIUC, 2014. https://opensiuc.lib.siu.edu/dissertations/886.

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Social networks tend to shape our views about the world. Our study conducts an empirical analysis of social network dynamics using Twitter data. We ask whether social networks influence voting decisions, and determine whether or not people make consistent choices based on their tweets or what they believe. We collect Twitter data on a daily basis, with dynamic social network measurements before, during, and after the 2012 Presidential election. We identify how people should believe based on their ideological profiles. We use lexicographical analysis to check if ideological key words are present in a user's tweets, and if the overall sentiment on this issue is positive or negative. We utilize this data to determine how people should have chosen an outcome which may conflict with an individual's observed declaration of political ideology. We are able to determine what percentage of the population made a consistent choices based on their Tweets during the 2012 presidential election. Additionally, we examine the social network structure in Twitter and how it affects voting. We illustrate that an individual's political ideology is influenced by others in their network.\\ Consumer confidence is an economic indicator which measures the degree of optimism that consumers feel about the overall state of the economy as well as their personal financial situation. We will show that consumer sentiment can be measured via analysis of social networks. Specifically, we perform a lexicographic analysis of Twitter data over a three month period. After careful analysis, we find that not only does talk intensity of economic issues cause shifts in the daily stock market prices but has a significant negative affect.\\ The study of religion has enjoyed distinction and legitimacy within sociology, psychology, anthropology, and political science for many years. This paper concerns the extent to which economic opinion is embedded in structure of religious social relations. We hope to enhance the empirical study of homophily and the economics of behavior by showing how beliefs, norms, and values are affected by religion and, by extension, morals, and culture. We utilize a technique called cluster analysis to determine homophilic ties within a single attribute, religiosity. We see that religion affects economic attitudes and activities of individuals, groups, and societies. Further, religion influences how behavior and institutions are affected by social relations and in our case homophily. This influence is one of the classic questions of social theory.

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Kim, Sungmin. "Community Detection in Directed Networks and its Application to Analysis of Social Networks." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1397571499.

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Isah, Haruna. "Social Data Mining for Crime Intelligence: Contributions to Social Data Quality Assessment and Prediction Methods." Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/16066.

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With the advancement of the Internet and related technologies, many traditional crimes have made the leap to digital environments. The successes of data mining in a wide variety of disciplines have given birth to crime analysis. Traditional crime analysis is mainly focused on understanding crime patterns, however, it is unsuitable for identifying and monitoring emerging crimes. The true nature of crime remains buried in unstructured content that represents the hidden story behind the data. User feedback leaves valuable traces that can be utilised to measure the quality of various aspects of products or services and can also be used to detect, infer, or predict crimes. Like any application of data mining, the data must be of a high quality standard in order to avoid erroneous conclusions. This thesis presents a methodology and practical experiments towards discovering whether (i) user feedback can be harnessed and processed for crime intelligence, (ii) criminal associations, structures, and roles can be inferred among entities involved in a crime, and (iii) methods and standards can be developed for measuring, predicting, and comparing the quality level of social data instances and samples. It contributes to the theory, design and development of a novel framework for crime intelligence and algorithm for the estimation of social data quality by innovatively adapting the methods of monitoring water contaminants. Several experiments were conducted and the results obtained revealed the significance of this study in mining social data for crime intelligence and in developing social data quality filters and decision support systems.

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Xu, Hailu. "Efficient Spam Detection across Online Social Networks." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470416658.

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Fares, Julian. "Modelling Stakeholder Integration Using Social Networks: An Australian Integrated Health Care Project." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20455.

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Stakeholders form relationships in projects to achieve both personal and project objectives. Proper stakeholder identification, categorisation and engagement methods that capture the social processes of the stakeholder network environment are lacking in many project management standards. In this study, social network theories and analytics are introduced as a new lens for stakeholder analysis to examine an integrated network of health care stakeholders (health care services and providers) that provides care for patients. The aim is to identify influential key stakeholders and determine the optimal network structure and composition for stakeholder integration (integrated care). A quantitative, whole network study was conducted where 56 health care providers were asked to report on their network relationships and the extent to which services are integrated in a geographic region in NSW, Australia. The results show that social network structure, position and relation constructs have a vital role in integrating health care stakeholders. More precisely, it was shown that ego-density, degree and betweenness centrality, tie strength and functional diversity have a positive association with service integration. In contrast, network efficiency, constraint and reciprocated relationships were found to be negatively associated with service integration. The research implications for the project management community are that stakeholders can be analysed and managed according to their relational attributes. With respect to integrated care, all stakeholders involved in integrated care projects should consider relationships configurations in their integration endeavour. Social network analysis is shown to be a vital tool for evaluating service integration where it identifies which services are currently working together; which ones are not working with others; where are the gaps in the relationships that can be strengthened and addressed.

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Greschbach, Benjamin. "Privacy Analysis and Protocols for Decentralized Online Social Networks." Licentiate thesis, KTH, Teoretisk datalogi, TCS, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-165377.

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Decentralized Online Social Networks (DOSNs) are evolving as a promising approach to mitigate design-inherent privacy flaws of logically centralized services such as Facebook, Google+ or Twitter. Common approaches to implement a DOSN build upon a peer-to-peer (P2P) architecture in order to avoid the central aggregation of sensitive user data at one provider-controlled location. While the absence of a single point of data aggregation strikes the most powerful attacker from the list of adversaries, the decentralization also removes some privacy protection afforded by the provider's intermediation of all communication in a centralized Online Social Network (OSN). As content storage, access right management, retrieval and other administrative tasks of the service become the obligation of the users, it is non-trivial to hide the metadata of objects and information flows, even when the content itself is encrypted. Such metadata is, deliberately or as a side effect, hidden by the provider in a centralized system. Implementing the different features of a privacy-presvering DOSN does not only face these general challenges but must also cope with the absence of a trusted agent with full access to all data. For example user authentication should provide the same usabilty known from common centralized OSN services, such as ease of changing a password, revoking the access of a stolen device or resetting a forgotten password via e-mail or security questions. All this without relying on a trusted third party such as an identity provider. Another example is user search, where the challenge is to protect user data while making user findable at the same time. An implementation of such a feature in a DOSN has to work without assuming a trusted provider having access to all user profiles maintaining a global search index. In this work we analyze the general privacy-problems in a DOSN, especially those arising from metadata. Furthermore, we suggest two privacy-preserving implementations of standard OSN features, i.e., user authentication via password-login and user search via a knowledge threshold. Both implementations do not rely on a trusted, central provider and are therefore applicable in a DOSN cenario but can be applied in other P2P or low-trust environments as well.
I dagens populära sociala nätverkstjänster, såsom Facebook, Google+ och Twitter, finns en risk för integritetskränkningar. Risken är en oundviklig konsekvens av den logiskt centraliserade struktur som dessa tjänster bygger på. Decentraliserade sociala nätverkstjänster (eng. Decentralized Online Social Networks, DOSNs) är en lovande utveckling för att minska risken och skydda användarnas personliga information från tjänsteleverantören och dem som leverantören samarbetar med. Ett vanligt sätt att implementera ett DOSN är genom en icke-hierarkisk nätverksarkitektur (eng. peer-to-peer network) för att undvika att känsliga personuppgifter ansamlas på ett ställe under tjäns televerantörens kontroll. Att inte längre ha en tjänsteleverantör som har tillgång till alla data tar bort den största risken för integritetskränkningar. Men genom att ersätta den centrala tjänsteleverantören med ett decentraliserat system tar vi även bort visst integritetsskydd. Integritetsskyddet var en konsekvens av att förmedlingen av all användarkommunikation skedde genom tjänsteleverantörens mellanservrar. När ansvaret för lagring av innehållet, hantering av behörigheterna, åtkomst och andra administrativa uppgifter övergår till användarna själva, då blir det en utmaning att skydda metadata för objekten och informationsflöden, även om innehållet är krypterat. I ett centraliserat system är dessa metadata faktiskt skyddade av tjänsteleverantören - avsiktligt eller som en sidoeffekt. För att implementera de olika funktioner som ska finnas i ett integritetsskyddande DOSN, är det nödvändigt att både lösa dessa generella utmaningar och att hantera frånvaron av ett betrodd tredjepart som har full tillgång till all data. Autentiseringen av användarna, till exempel, borde ha samma användbarhet som finns i centraliserade system. Det vill säga att det är lätt att ändra lösenordet, dra tillbaka rättigheterna för en stulen klientenhet, eller återställa ett glömt lösenord med hjälp av e-post eller säkerhetsfrågor - allt utan att förlita sig på en betrodd tredjepart. Ett annat exempel är funktionen att kunna söka efter andra användare. Utmaningen där är att skydda informationen om användarna samtidigt som det måste vara möjligt att hitta användare baserad på samma information. En implementation av denna funktion i ett DOSN måste klara sig utan en betrodd tjänsteleverantör som med tillgång till alla användares data kan upprätthålla ett globalt sökindex. I den här avhandlingen analyserar vi de generella risker för integritetskränkningar i DOSN, särskilt de som orsakas av metadata. Dessutom föreslår vi två integritetskyddande implementationer av vanliga funktioner i en socialt nätverkstjänst: lösenordbaserad användarautentisering och en användarsökfunktionen med en kunskaptröskel. Båda implementationerna är lämpliga för DOSN-scenarier eftersom de klarar sig helt utan en betrodd, central tjänstleverantör, och kan därför också användas i andra sammanhang: såsom icke-hierarkiska nätverk eller andra system som måste klara sig utan en betrodd tredjepart.

QC 20150428

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Esfandyari, A. "MULTIDIMENSIONAL ANALYSIS OF PEOPLE'S BEHAVIOR IN ONLINE SOCIAL NETWORKS." Doctoral thesis, Università degli Studi di Milano, 2017. http://hdl.handle.net/2434/470004.

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L’impressionante crescita in popolarità delle Online Social Networks (OSNs), evidenziata dall’enorme numero di utenti oggi legati ai social network più popolari, offre un’opportunità unica per comprendere i comportamenti online degli individui. In questa tesi, analizziamo i comportamenti delle persone sulle OSNs considerando che tali comportamenti sono il risultato della combinazione di esperienze ed attitudini sia online che offline. Dapprima, eseguiamo una analisi multidimensionale degli utenti attraverso diversi social media per fornire una descrizione complessiva dei comportamenti online e comprendere come questi cambino quando più media sono disponibili contemporaneamente. I risultati che presentiamo rappresentano uno dei primi esempi di esplorazione dei comportamenti umani su diversi social media. Ad esempio, utilizzando lo user degree su 5 diversi social network, evidenziamo che l’importanza di ogni individuo cambia da piattaforma a piattaforma. La natura longitudinale del nostro dataset è anche stata sfruttata per studiare l’attività di posting degli utenti, evidenziando una leggera correlazione positiva sulla frequenza con cui gli utenti pubblicano su social media differenti e confermando la natura bursty delle attività di posting mediante l’uso di serie temporali multidimensionali. Inoltre, durante la tesi abbiamo sviluppato un metodo di identificazione innovativo per collegare le persone attraverso le diverse piattaforme social. Facendo riferimento agli attributi pubblici comuni, attraverso l’uso di application programming interface (API) dei diversi social network, costruiamo le istanze negative in tre modi diversi, superando la selezione randomica abitualmente adottata, allo scopo di valutare la robustezza del nostro algoritmo di identificazione su diversi dataset. I risultati mostrano che l’approccio porta ad un metodo di identificazione molto efficace per costruire dataset affidabili. Uno scenario reale costruito su Google+ e Facebook è stato utilizzato come testbed per la validazione del metodo. I risultati che riportiamo dimostrano i vantaggi ottenibili con il nuovo metodo rispetto ad altri metodi da letteratura. Infine, la tesi compie un primo passo verso una miglior comprensione degli effetti degli eventi offline sulla struttura del grafo delle social network in cui sono pubblicizzati. Più precisamente, svolgiamo una analisi temporale della social network legata all’evento, comprendendo le persone che dichiarano di partecipare all’evento tramite facebook, e valutiamo come questa evolva durante l’intervallo temporale dell’eventi stesso. I risultati mostrano che nuove amicizie nascono durante l’evento e che la creazione di questi nuovi legami sociali è una delle cause principali di chiusura triangolare e che il grado maggiore si osserva durante l’ultimo giorno dell’evento stesso.
The unprecedented and quickly increasing popularity of Online Social Networks (OSNs) is evidenced by the huge number of users who are turning to Facebook, Twitter and other social networks. The rapid growth of these online social networks provides a unique chance to study and understand the online behavior of the people. In this thesis, we analyze people's behavior in online social network considering the fact that online behavior of people is influenced by different factors which derive from the combination of their offline and online life. First, we perform a multidimensional analysis of users across multiple social media sites to give an all-around picture of people’s online behavior. While people in their online life have access to a wide portfolio of social platforms, little is known about users’ behavior when they have different online communication media available. Our findings represent some novel insights about people’s behavior across social media. Having at our disposal users’ degree in five different social networks, we find that the individuals’ importance changes from medium to medium. The longitudinal nature of our dataset has been exploited to investigate the posting activity. We find a slightly positive correlation on how often users publish on different social media and we confirm the burstiness of the posting activities extending it to multidimensional time-series. Second, we develop an innovative identification methodology for connecting people across multiple social platforms. Relying on common public attributes available through the official application programming interface (API) of social networks, we construct negative instances in three different ways, going beyond the commonly adopted random selection to evaluate the robustness of our identification algorithm on different datasets. Results show that the approach can lead to a very effective identification method and methodology for building reliable datasets. Moreover, we analyzed the success of our method in a real scenario built on Google+/Facebook neighborhoods. Experiments reveal the advantages of the proposed method in comparison to previous methods in the literature. Finally, we take the first step towards understanding the effect of offline events on the graph structure of the social network where they are advertised. More precisely, we perform a temporal analysis of the event social network, constituted by people declaring to attend the event on Facebook and the links between them, and evaluated how it evolves during the event time period. The results show that new friendships are created during events and that this new friendships creation is one of the main reasons of triangle closure and the higher degrees observed in the last day of the events period.

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POZZI, FEDERICO ALBERTO. "Probabilistic Relational Models for Sentiment Analysis in Social Networks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/65709.

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The huge amount of textual data on theWeb has grown in the last few years rapidly creating unique contents of massive dimensions that constitutes fertile ground for Sentiment Analysis. In particular, social networks represents an emerging challenging sector where the natural language expressions of people can be easily reported through short but meaningful text messages. This unprecedented contents of huge dimensions need to be efficiently and effectively analyzed to create actionable knowledge for decision making processes. A key information that can be grasped from social environments relates to the polarity of text messages, i. e. the sentiment (positive, negative or neutral) that the messages convey. However, most of the works regarding polarity classification usually consider text as unique information to infer sentiment, do not taking into account that social networks are actually networked environments. A representation of real world data where instances are considered as homogeneous, independent and identically distributed (i.i.d.) leads us to a substantial loss of information and to the introduction of a statistical bias. For this reason, the combination of content and relationships is a core task of the recent literature on Sentiment Analysis, where friendships are usually investigated to model the principle of homophily (a contact among similar people occurs at a higher rate than among dissimilar people). However, paired with the assumption of homophily, constructuralism explains how social relationships evolve via dynamic and continuous interactions as the knowledge and behavior that two actors share increase. Considering the similarity among users on the basis of constructuralism appears to be a much more powerful force than interpersonal influence within the friendship network. As first contribution, this Ph.D. thesis proposes Approval Network as a novel graph representation to jointly model homophily and constructuralism, which is intended to better represent the contagion on social networks. Starting from the classical state-of-the-art methodologies where only text is used to infer the polarity of social networks messages, this thesis presents novel Probabilistic Relational Models on user, document and aspect-level which integrate the structural information to improve classification performance. The integration is particularly useful when textual features do not provide sufficient or explicit information to infer sentiment (e. g., I agree!). The experimental investigations reveal that incorporating network information through approval relations can lead to statistically significant improvements over the performance of complex learning approaches based only on textual features.

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Kulkarni, Rohan A. "Coolhunting and Coolfarming : harnessing the power of collaborative innovation networks using social network analysis." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90713.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 71-73).
Email, Instant Messaging, Voice Over IP (VOIP) and other means of online communication have become so ubiquitous today that we rarely take a moment to acknowledge how the internet has changed and redefined the ways in which we communicate and collaborate with fellow human beings. The internet has empowered us to collaborate with others in ways that were not possible till just a few years ago. As we communicate and interact with each other and form relationships, we weave intricate Social Networks that can be analyzed and exhibit communication patterns that can be quantified. In this thesis I have applied Social Network Analysis based techniques that constitute Coolhunting (Gloor & Cooper, 2007) to analyze E-Mail and WebEx communications of sales professionals of a large technology company. I have quantified communication patterns and computed metrics of social network prominence such as degree and betweenness centralities using Condor, a Social Network Analysis and Coolhunting software. Several significant correlations between the success of sales professionals and these quantified communication patterns and centrality measures were found. The communication patterns and centralities of the sales professionals exhibited several traits of Collaborative Innovation Networks or COINs (Gloor, 2006). I have assessed the implications of these communication patterns and correlations and applied the concept of Coolfarming (Gloor, 2011 a) to make recommendations to the technology company on how it could leverage the power of these COINs to their advantage. Key Terms: Collaborative Innovation Networks (COINs), Coolhunting, Coolfarming, Social Network Analysis, Condor, E-Mail, WebEx
by Rohan Kulkarni.
S.M. in Engineering and Management

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Marra, Marianna. "Indirect ties in knowledge networks : a social network analysis with ordered weighted averaging operators." Thesis, Aston University, 2015. http://publications.aston.ac.uk/25303/.

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This PhD thesis analyses networks of knowledge flows, focusing on the role of indirect ties in the knowledge transfer, knowledge accumulation and knowledge creation process. It extends and improves existing methods for mapping networks of knowledge flows in two different applications and contributes to two stream of research. To support the underlying idea of this thesis, which is finding an alternative method to rank indirect network ties to shed a new light on the dynamics of knowledge transfer, we apply Ordered Weighted Averaging (OWA) to two different network contexts. Knowledge flows in patent citation networks and a company supply chain network are analysed using Social Network Analysis (SNA) and the OWA operator. The OWA is used here for the first time (i) to rank indirect citations in patent networks, providing new insight into their role in transferring knowledge among network nodes; and to analyse a long chain of patent generations along 13 years; (ii) to rank indirect relations in a company supply chain network, to shed light on the role of indirectly connected individuals involved in the knowledge transfer and creation processes and to contribute to the literature on knowledge management in a supply chain. In doing so, indirect ties are measured and their role as means of knowledge transfer is shown. Thus, this thesis represents a first attempt to bridge the OWA and SNA fields and to show that the two methods can be used together to enrich the understanding of the role of indirectly connected nodes in a network. More specifically, the OWA scores enrich our understanding of knowledge evolution over time within complex networks. Future research can show the usefulness of OWA operator in different complex networks, such as the on-line social networks that consists of thousand of nodes.

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Davel, Ronél. "Enriching knowledge networks - considering synergies between social network analysis, communities of practice and knowledge." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/61289.

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The constructive management of existing knowledge and the access to and development of new knowledge has become essential to organisations. Since tacit knowledge can often not be captured or documented, knowledge is often created and shared through social interaction within organisations. Relationships are thus fundamental to knowledge creation and knowledge transfer and the various forms of social networks existing within organisations play a primary role in leveraging these relationships. This study followed the socialisation philosophy as reflected in the works of Nonaka and Takeuchi (1995) and Hansen et al. (1999), where the creation and sharing of knowledge occurs primarily by way of social interaction between individuals. The said interaction typically occurs within informal networks, also known as knowledge networks (Helms & Buijsrogge 2006). In recent times there has been a growing awareness of social network analysis (SNA) as an instrument to plot knowledge and expertise as well as to confirm the character of connections in informal networks (Cross et al. 2004; Chan & Liebowitz 2006; Müller-Prothmann 2006; Murale & Raju 2013; Cooke & Hall 2013; D'Errico et al. 2014). In line with the aforementioned studies, this study intended to investigate how the integration of networking into KM can produce significant advantages for organisations. This research intended to outline a method for organisations to strengthen their social capital by analysing, shaping and reinforcing their knowledge networks, thereby enhancing the manner in which they share and create knowledge. Subsequently the main research problem of this study was to investigate how knowledge networks can be improved as a result of synergies between SNA, CoPs and knowledge maps. The researcher attempted to illustrate via this question that cultivating synergies between SNA, CoPs and knowledge maps will enable organisations to produce stronger knowledge networks and ultimately increase their social capital. In order to execute this study, the researcher developed a process map with the aim of demonstrating exactly how knowledge networks could be advanced as a result of synergies between SNA, CoPs and knowledge maps. This process map - which answers the "how" in this question - is presented as the new contribution that this study makes towards any organisation wanting to reinforce knowledge networks. It is anticipated that this research will enable organisations to enrich their knowledge networks and expand their social capital by building on the process map that was developed and implemented in this study.
Thesis (PhD)--University of Pretoria, 2017.
Information Science
PhD
Unrestricted

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Grant, Eli. "Network analysis for social programme evaluation." Thesis, University of Oxford, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.719991.

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Stearmer, Steven Matthew Ph D. "Diaspora Social Movements in Cyberspace: Epistemological and Ethnographic Considerations." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1460658606.

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Kochar, Shilpa. "Network ties and their effect on employee collaboration in enterprise social networks." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/210864/1/Shilpa_Kochar_Thesis.pdf.

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There has been a rapid growth and widespread adoption of social media technologies across all industries. Despite the growing importance of enterprise social networks (ESN), there has been limited research in examining the role of employee relationships (ties) in these networks. To gain an in-depth understanding of ties and collaboration outcomes, a mixed method research was conducted. StackExchange data was collected and processed, social network analysis and qualitative analysis of data has been done and the findings are presented in the form of an empirically derived theoretical model. Study provides novel insights into importance of negative ties and reciprocal ties.

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Nordvik, Monica K. "Contagious Interactions : Essays on social and epidemiological networks." Doctoral thesis, Stockholm : Visby : Acta Universitatis Stockholmiensis ; eddy.se [distributör], 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-8309.

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Lam, Phuongthao Tuyen. "Examining Sexually Transmitted Disease Transmission Dynamics in Chlamydia Positive and Negative Adolescent Population using Social Network Analysis." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/iph_theses/78.

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Adolescents are disproportionately affected by a wide range of STDs due to high level of personal risk behaviors and poor access to STD prevention services. As documented in numerous previous studies, STDs could lead to many serious consequences to adolescents’ health and the overall well being of society. One prominent concern is that STDs increase adolescent’s risk in acquiring HIV infection. Among all STDs, Chlamydia is the most prevalent in adolescents as well as in the general population. No previous studies have attempted to examine the social interaction of adolescent population heavily affected by Chlamydia. In this study, we would like to take a step forward to identify the difference in behavioral risk level between Chlamydia positive and negative adolescent social network and to describe any impacts of these groups on the transmission of other STDs using social network analysis of data collected from adolescent population in Dekalb County, Georgia. The results indicated highest behavioral risk in the negative girl index respondents’ contacts followed by those of positive boys, positive girls and finally negative boys. However STD prevalence in the contacts among these different groups did not follow the same pattern. Prevalence of STD is highest in the negative girls’ contact group followed by that of positive boys, negative boys; and interestingly positive boys’ contacts exhibit the lowest STD rate. As informed by the results, the presence of infection is not a sufficient indicator of risks; thus, network characteristic was also examined to accurately determine transmission dynamics in this population. Social and sexual network structures among these four different index groups and their contacts suggested low level of STD transmission.

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Jones, Simon. "Automating group-based privacy control in social networks." Thesis, University of Bath, 2012. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629649.

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Users of social networking services such as Facebook often want to manage the sharing of information and content with diﬀerent groups of people based on their diﬀering relationships. The growing popularity of such services has meant that users are increasingly faced with the copresence of diﬀerent groups associated with diﬀerent aspects of their lives, within their network of contacts. However, few users are utilising the group-based privacy controls provided to them by the SNS provider. In this thesis we examine the reasons behind the lack of use of group-based privacy controls, ﬁnding that it can be largely attributed to the signiﬁcant burden associated with group conﬁguration. We aim to overcome this burden by developing automated mechanisms to assist users with many aspects of group-based privacy control, including initial group conﬁguration, labeling, adjustment and selection of groups for sharing privacy sensitive content. We use a mixed methods approach in order to understand: how automated mechanisms should be designed in order to support users with their privacy control, how well these mechanisms can be expected to work, what the limitations are, and how such mechanisms aﬀect users’ experiences with social networking services and content sharing. Our results reveal the criteria that SNS users employ in order to conﬁgure their groups for privacy control and illustrate that oﬀ-the-shelf algorithms and techniques which are analogous to these criteria can be used to support users. We show that structural network clustering algorithms provide beneﬁts for initial group conﬁguration and that clustering threshold adjustments and detection of hubs and outliers with the network are necessary for group adjustment. We demonstrate that public proﬁle data can be extracted from the network in order to help users to comprehend their groups, and that contextual information relating to context, contacts, and content can be used to make recommendations about which groups might be useful for disclosure in a given situation. We also show that all of these mechanisms can be used to signiﬁcantly reduce the burden of privacy control and that users react positively to such features.

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Erdogan, Idil Ekim. "Sex differences and multiplexity in Swedish local elite networks." Thesis, Linnéuniversitetet, Institutionen för samhällsstudier (SS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-89696.

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This study discovers sex differences in multiplex links on formal and informal networks of Swedish local elite. Elites are widely known to have an immense influence on a country’s politics and governance, and proportional representation of women in elite positions is an indicator of democratization and gender equality. Sweden has long been known for democratic and gender equal regulations, and women occupy more elite positions relative to other countries, yet they are still heavily underrepresented in the elite. Previous research on Swedish local elite revealed that women in the elite do not differ from their male peers in terms of local network properties on formal and informal networks; however, the circumstances on the multiplex links are unknown. In this study, multiplexity approach is adopted as it is known for allowing to capture social processes in social network analysis, which could otherwise be overlooked. The formal and informal networks of the community elite from four mid-sized municipalities in Västra Götaland region in Sweden are transformed into multiplex networks, and they were analyzed for local network configurations by using exponential random graph model (ERGM) estimation method. The findings showed that women in the community elite tend to have more multiplex relationships than men; however, they significantly lack valuable brokerage positions on the multiplex level compared to men. Male closure on the multiplex level was found to be higher than females at a partially significant rate, and gender-based homophily on multiplex networks was not found to be statistically significant. One implication of the study is women’s position and integration in the community elite do not appear identical to men’s, and women’s access to social capital in the elite networks is more constrained than it was presumed previously. Another implication is that special attention should be paid to multiplexity in social network analysis research, as it is a valuable tool for improving the apprehension of social mechanisms.

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Alahmadi, Dimah. "Recommender systems based on online social networks : an Implicit Social Trust And Sentiment analysis approach." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/recommender-systems-based-on-online-social-networks-an-implicit-social-trust-and-sentiment-analysis-approach(ac03f7e5-4fc0-4c4a-bace-82188823eb84).html.

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Recommender systems (RSs) provide personalised suggestions of information or products relevant to user's needs. RSs are considered as powerful tools that help users to find interesting items matching their own taste. Although RSs have made substantial progress in theory and algorithm development and have achieved many commercial successes, how to utilise the widely available information on Online Social Networks (OSNs) has largely been overlooked. Noticing this gap in existing research on RSs and taking into account a user's selection being greatly influenced by his/her trusted friends and their opinions, this thesis proposes a novel personalised Recommender System framework, so-called Implicit Social Trust and Sentiment (ISTS) based RSs. The main motivation was to overcome the overlooked use of OSNs in Recommender Systems and to utilise the widely available information from such networks. This work also designs solutions to a number of challenges inherent to the RSs domain, such as accuracy, cold-start, diversity and coverage. ISTS improves the existing recommendation approaches by exploring a new source of data from friends' short posts in microbloggings. In the case of new users who have no previous preferences, ISTS maps the suggested recommendations into numerical rating scales by applying the three main components. The first component is measuring the implicit trust between friends based on their intercommunication activities and behaviour. Owing to the need to adapt friends' opinions, the implicit social trust model is designed to include the trusted friends and give them the highest weight of contribution in recommendation encounter. The second component is inferring the sentiment rating to reflect the knowledge behind friends' short posts, so-called micro-reviews. The sentiment behind micro-reviews is extracted using Sentiment Analysis (SA) techniques. To achieve the best sentiment representation, our approach considers the special natural environment in OSNs brief posts. Two Sentiment Analysis methodologies are used: a bag of words method and a probabilistic method. The third ISTS component is identifying the impact degree of friends' sentiments and their level of trust by using machine learning algorithms. Two types of machine learning algorithms are used: classification models and regressions models. The classification models include Naive Bayes, Logistic Regression and Decision Trees. Among the three classification models, Decision Trees show the best Mean absolute error (MAE) at 0.836. Support Vector Regression performed the best among all models at 0.45 of MAE. This thesis also proposes an approach with further improvement over ISTS, namely Hybrid Implicit Social Trust and Sentiment (H-ISTS). The enhanced approach applies improvements by optimising trust parameters to identify the impact of the features (re-tweets and followings/followers list) on recommendation results. Unlike the ISTS which allocates equal weight to trust features, H-ISTS provides different weights to determine the different effects of the two trust features. As a result, we found that H-ISTS improved the MAE to be 0.42 which is based on Support Vector Regression. Further, it increases the number of trust features from two to five features in order to include the influence of these features in rating predictions. The integration of the new approach H-ISTS with a Collaborative Filtering recommender system, in particular memory-based, is investigated next. Therefore, existing users with a history of ratings can receive recommendations by fusing their own tastes and their friends' preferences using the two type of memory-based methods: user-based and item-based. H-ISTSitem is the integration of H-ISTS and item-based which provides the lowest error at 0.7091. The experiments show that diversity is better achieved using the H-ISTSuser which is the integration of H-ISTS and user-based technique. To evaluate the performance of these approaches, two real social datasets are collected from Twitter. To verify the proposed framework, the experiments are conducted and the results are compared against the most relevant baselines which confirmed that RSs have been successfully improved using OSNs. These enhancements demonstrate the effectiveness and promises of the proposed approach in RSs.

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Han, Xiao. "Mining user similarity in online social networks : analysis,modeling and applications." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0013/document.

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Réseaux sociaux (RS) (par exemple, Facebook, Twitter et LinkedIn) ont gagné en popularité écrasante et accumulé des données numériques massives sur la société humaine. Ces données massives, représentant de l’information personnelle et sociale des individus, nous offrent des possibilités sans précédent pour étudier, analyser et modéliser la structure de réseau complexe, les relations humaines, les gens similitude, etc. Pendant ce temps, les RS ont déclenché un grand nombre d’applications et de services qui rentables chercher à maintenir des liens de vibrer et l’expérience des utilisateurs d’avance. Dans ce contexte, comment concevoir ces applications et les services, en particulier comment extraire et d’exploiter des fonctionnalités sociales efficaces à partir des données massives disponibles pour améliorer les applications et les services, a reçu beaucoup d’attention. Cette thèse, visant à améliorer les applications et les services sociaux, étudie trois questions essentielles et pratiques RS: (1) Comment pouvons-nous explorer les amis potentiels pour un utilisateur d’établir et d’élargir ses liens sociaux? (2) comment pouvons-nous découvrir un contenu intéressant pour un utilisateur pour satisfaire ses goûts personnels? (3) comment pouvons-nous informer un utilisateur du risque d’exposition de son information privée pour préserver sa vie privée? S’appuyant sur les idées sur la similarité de personnes dans les sciences sociales, cette thèse étudie les effets et les applications de l’utilisateur similitude dans les RS pour résoudre les problèmes mentionnés ci-dessus. Plus précisément, les sociologues suggèrent que la similitude engendre connexion et induit principe homophilie que les gens similaires (par exemple, même âge, l’éducation ou la profession) sont plus susceptibles de communiquer, de confiance et de partager l’information avec l’autre que ceux dissemblables. Inspiré par ces résultats, cette thèse étudie le principe de similitude répandue dans RS en termes de savoir si les utilisateurs similaires seraient proches dans leurs relations sociales, similaire dans leurs intérêts, ou approximative dans leur géo distance, en se appuyant sur 500K profils d’utilisateurs recueillies auprès de Facebook; il explore en outre des solutions pour exploiter efficacement le principe de similitude observée pour concevoir les quatre applications et des services sociaux suivantes: • Effets de Similarité de L’utilisateur sur Lien Prévision pour les Nouveaux Utilisateurs : nous analysons la prédiction de liaison pour les nouveaux utilisateurs qui n’ont pas créé de lien. Basé sur l’information limitée obtenu lors de votre inscription la procédure de nouveaux utilisateurs, ainsi que les attributs et les liens des utilisateurs existants dans un RS, nous étudions la façon dont beaucoup de similitude entre deux utilisateurs affecterait la probabilité qu’ils se lient d’amitié. En conséquence, nous proposons un modèle de prédiction de liaison efficace pour les nouveaux utilisateurs. • Similarité Minière de L’utilisateur pour la Découverte de Contenu en Réseaux P2P Sociale : nous examinons comment similarité et connaissances des participants dans RS pourraient bénéficier leur découverte de contenu dans les réseaux P2P. Nous construisons un modèle de réseau P2P sociale où chaque pair attribue plus de poids à ses amis dans RS qui ont similarité supérieur et plus de connaissances. Utilisation de marche aléatoire avec la méthode de redémarrage, nous présentons un nouveau contenu algorithme de découverte le dessus du modèle de réseau P2P sociale proposé. • Inspection intérêt similarité - Prédiction et Application : nous présentons des études empiriques détaillées sur les intérêts similitude et de révéler que les gens sont susceptibles de présenter des goûts similaires s’ils ont des informations démographiques similaires (par exemple, âge, lieu), ou s’elles sont amis. Par conséquent, étant donné un nouvel utilisateur dont les intérêts (...)
Online Social Networks (OSNs) (e.g., Facebook, Twitter and LinkedIn) have gained overwhelming popularity and accumulated massive digital data about human society. These massive data, representing individuals' personal and social information, provide us with unprecedented opportunities to study, analyze and model the complex network structure, human connections, people similarity, etc. Meanwhile, OSNs have triggered a large number of profitable applications and services which seek to maintain vibrate connections and advance users' experience. In this context, how to devise such applications and services, especially how to extract and exploit effective social features from the massive available data to enhance the applications and services, has received much attention. This dissertation, aiming to enhance the social applications and services, investigates three critical and practical issues in OSNs: (1) How can we explore potential friends for a user to establish and enlarge her social connections? (2) How can we discover interesting content for a user to satisfy her personal tastes? (3) How can we inform a user the exposure risk of her private information to preserve her privacy? Drawing on the insights about people's similarity in social science, this dissertation studies the widespread similarity principle in OSN in terms of whether similar users would be close in their social relationships, similar in their interests, or approximate in their geo-distance, relying on 500K user profiles collected from Facebook; it further explores solutions to effectively leverage the observed similarity principle to address the aforementioned practical issues

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Munasib, Abdul B. A. "Lifecycle of social networks: A dynamic analysis of social capital accumulation." The Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1121441394.

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Rosen, Joel Louis. "Friends with benefits : an investigation into the social dynamics of network creation in the born-global SME." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/22826.

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Previous literature on the social dynamics of network creation in enterprises has drawn a sharp division between the utility of personal and professional networks. This has been particularly marked in social network analysis of born-global SMEs operating in emerging markets and seeking to internationalise. Using the case study of International Housing Solutions (Pty) Ltd (IHS) – a born global SME with both a global and a regional network – this research creates a deeper and more nuanced understanding of what such networks look like, what human factors are key to their operation, and what the relative importance is of the personal and professional drivers of networking.The study employs a mixed-method research design including network mapping and both qualitative and quantitative analysis of questionnaire responses from 35 participants in the IHS network, providing both hard data and rich qualitative insights into the ingredients and processes required for effective networking in such an enterprise.The results provide robust evidence for crossover between professional and personal networking activities; both are equally relevant in enabling the born-global SME to grow networks, increase innovation and enter otherwise impenetrable markets. Though the weighting of networking attributes is marginally different – for personal networks, the key attributes are advice, trust, friendship and communication; for professional networks, knowledge and referrals – in practice, both the personal and the professional are assimilated into a single complex of network activity and cannot be viewed in isolation.The research thus contributes innovative findings to a hitherto under-researched aspect of networking in the born-global SME.
Dissertation (MBA)--University of Pretoria, 2012.
Gordon Institute of Business Science (GIBS)
unrestricted

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Santoro, Lauren Ratliff. "Choosing to be Changed: How Selection Conditions the Effect of Social Networks on Political Attitudes." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1498740182855649.

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Xia, Huadong. "Modeling, Analysis and Comparison of Large Scale Social Contact Networks on Epidemic Studies." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/51672.

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Social contact networks represent proximity relationships between individual agents. Such networks are useful in diverse applications, including epidemiology, wireless networking and urban resilience. The vertices of a social contact network represent individual agents (e.g. people). Time varying edges represent time varying proximity relationship. The networks are relational -- node and edge labels represent important demographic, spatial and temporal attributes. Synthesizing social contact networks that span large urban regions is challenging for several reasons including: spatial, temporal and relational variety of data sources, noisy and incomplete data, and privacy and confidentiality requirements. Moreover, the synthesized networks differ due to the data and methods used to synthesize them. This dissertation undertakes a systematic study of synthesizing urban scale social contact networks within the specific application context of computational epidemiology. It is motivated by three important questions: (i) How does one construct a realistic social contact network that is adaptable to different levels of data availability? (ii) How does one compare different versions of the network for a given region, and what are appropriate metrics when comparing the relational networks? (iii) When does a network have adequate structural details for the specific application we have. We study these questions by synthesizing three social contact networks for Delhi, India. Our case study suggests that we can iteratively improve the quality of a network by adapting to the best data sources available within a framework. The networks differ by the data and the models used. We carry out detailed comparative analyses of the networks. The analysis has three components: (i) structure analysis that compares the structural properties of the networks, (ii) dynamics analysis that compares the epidemic dynamics on these networks and (iii) policy analysis that compares the efficacy of various interventions. We have proposed a framework to systematically analyze how details in networks impact epidemic dynamics over these networks. The results suggest that a combination of multi-level metrics instead of any individual one should be used to compare two networks. We further investigate the sensitivity of these models. The study reveals the details necessary for particular class of control policies. Our methods are entirely general and can be applied to other areas of network science.
Ph. D.

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Kaushik, Sanjana. "Social Networks of Technology Caregivers and Caregivees." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613749933487134.

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Merrington, Shannon E. "Dark networks : criminal collaboration in Australian police forces." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/103632/1/Shannon%20Elizabeth_Merrington_Thesis.pdf.

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The study aimed to investigate why police officers engage in corruption, the role of trust in facilitating and bonding officers together to allow them to participate in large-scale or serious corruption, and the network structures that result from these relationships. The findings revealed that officers collaborated and operated under a network structure reinforced by a subculture of unwritten rules, codes and acceptance by senior officers. This network was found to be dynamic, shifting in structure, membership and activity, but remained highly clustered and cohesive around a few core actors in the network. Additionally, the corruption network operated on relationships based on collaborations of trust. Officers used trustworthiness attributes, personal experience and third party information to assess whether a fellow officer was trustworthy enough to be a member of the corruption network, which resulted in a ‘pipeline’ of trust.

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Vamadevan, Arimoto Miyuki. "Peer influence and adolescent substance use a social networks analysis /." Pullman, Wash. : Washington State University, 2010. http://www.dissertations.wsu.edu/Dissertations/Spring2010/m_Vamadevan_arimoto_050210.pdf.

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Dissertations / Theses on the topic 'Social Networks Analysis'

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