Relevant bibliographies by topics / Social Networks Analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Social Networks Analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Social Networks Analysis"

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Jadhav, Pranavati, and Dr Burra Vijaya Babu. "Detection of Community within Social Networks with Diverse Features of Network Analysis." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (December 31, 2019): 366–71. http://dx.doi.org/10.5373/jardcs/v11sp12/20193232.

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Davel, Ronel, Adeline S. A. Du Toit, and Martie M. Mearns. "Understanding Knowledge Networks Through Social Network Analysis." International Journal of Knowledge Management 13, no. 2 (April 2017): 1–17. http://dx.doi.org/10.4018/ijkm.2017040101.

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Social network analysis (SNA) is being increasingly deployed as an instrument to plot knowledge and expertise as well as to confirm the character of connections in informal networks within organisations. This study investigated how the integration of networking into KM can produce significant advantages for organisations. The aim of the research was to examine how the interactions between SNA, Communities of Practice (CoPs) and knowledge maps could potentially influence knowledge networks. The researchers endeavour 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. This article intends to present a process map that can be useful when an organisation wants to positively increase its social capital by examining influencing interactions between SNA, CoPs and knowledge maps, thereby enhancing the manner in which they share and create knowledge.
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Nasution, Mahyuddin K. M., Rahmad Syah, and Marischa Elveny. "Social Network Analysis: Towards Complexity Problem." Webology 18, no. 2 (December 23, 2021): 449–61. http://dx.doi.org/10.14704/web/v18i2/web18332.

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Social network analysis is a advances from field of social networks. The structuring of social actors, with data models and involving intelligence abstracted in mathematics, and without analysis it will not present the function of social networks. However, graph theory inherits process and computational procedures for social network analysis, and it proves that social network analysis is mathematical and computational dependent on the degree of nodes in the graph or the degree of social actors in social networks. Of course, the process of acquiring social networks bequeathed the same complexity toward the social network analysis, where the approach has used the social network extraction and formulated its consequences in computing.
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S, Santhosh Kumar, Vishnu Vardhan S, Wasim Jaffar M, Sultan Saleem A, and Sharmasth Vali Y. "Social Communicative Extraction Analysis." International Research Journal of Multidisciplinary Technovation 2, no. 4 (September 26, 2020): 4–10. http://dx.doi.org/10.34256/irjmt2042.

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The distinguishing proof of online networking networks has as of late been of significant worry, since clients taking an interest in such networks can add to viral showcasing efforts. Right now center around clients' correspondence considering character as a key trademark for recognizing informative systems for example systems with high data streams. We portray the Twitter Personality based Communicative Communities Extraction (T-PCCE) framework that recognizes the most informative networks in a Twitter organize chart thinking about clients' character. We at that point grow existing methodologies as a part of client’s character extraction by collecting information that speak to a few parts of client conduct utilizing AI strategies. We utilize a current measured quality based network discovery calculation and we expand it by embeddings a post-preparing step that dispenses with diagram edges dependent on clients' character. The adequacy of our methodology is exhibited by testing the Twitter diagram and looking at the correspondence quality of the removed networks with and without considering the character factor. We characterize a few measurements to tally the quality of correspondence inside every network. Our algorithmic system and the resulting usage utilize the cloud foundation and utilize the MapReduce Programming Environment. Our outcomes show that the T-PCCE framework makes the most informative networks.
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Thovex, Christophe, and Francky Trichet. "Semantic social networks analysis." Social Network Analysis and Mining 3, no. 1 (February 24, 2012): 35–49. http://dx.doi.org/10.1007/s13278-012-0055-y.

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Andryani, Ria, Edi Surya Negara, Rezki Syaputra, and Deni Erlansyah. "Analysis of Academic Social Networks in Indonesia." Qubahan Academic Journal 3, no. 4 (December 9, 2023): 409–21. http://dx.doi.org/10.58429/qaj.v3n4a289.

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Social network analysis to detect communities in social networks is a complex problem, this is due to differences in community definitions and the complexity of social networks. One of the social networks for researchers is the academic social network (ASN). We define the relationships between nodes in ASN into two forms, namely interconnection relationships and interaction relationships. Interconnection relationships are researchers' social relationships that are formed from similarities in discipline between researchers, while interaction relationships are researchers' social relationships that are formed through interactions carried out regarding joint article publications. This research aims to measure the social interactions and social interconnections of researchers in Indonesia using the social network analysis method. The ASN data used in this research comes from the academic social network Researchgate. This research produces information on the social networks of scientific groups in Indonesia and a framework for analyzing researchers' social networks using dual identification community mode which has been able to find and understand the structure of the research community based on records of interactions and interconnections with ASN with similarity values in both forms of network connections 85.9%.
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Yang, Hong Mei, Chun Ying Zhang, Rui Tao Liang, and Fang Tian. "Set Pair Social Network Analysis Model." Applied Mechanics and Materials 50-51 (February 2011): 63–67. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.63.

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Through the study on social network information, this paper explore that there exists the certain and uncertain phenomena in the process of finding the relationship between individuals by using social networks, and the social networks are constantly changing. In light of there are some uncertainty and dynamic problems for the network, this paper put forward the set pair social network analysis model and set pair social network analysis model and its properties.
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AYDIN, Nursen. "Social Network Analysis: Literature Review." AJIT-e Online Academic Journal of Information Technology 9, no. 34 (November 1, 2018): 73–80. http://dx.doi.org/10.5824/1309-1581.2018.4.005.x.

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In this article, social network analysis SNA is defined and historical development process is explained. A comprehensive literature search has been conducted for this purpose. SAA is a powerful method that centralizes individuals and their relations, in that the effect of the individual on the social network can be uncovered and the network of individual groups can be evaluated holistically. SNA shows the structural gaps and social capital in institutions, and focuses managers' attention on critical informal networks. Evaluating strategically important networks within an organization, make "invisible" groups visible in the interaction and allows them to work with key groups to facilitate effective collaboration.
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Skvoretz, John. "Pas de Deux: Social Networks and Network Analysis." Contemporary Sociology: A Journal of Reviews 37, no. 5 (September 2008): 423–26. http://dx.doi.org/10.1177/009430610803700511.

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Himelboim, Itai, Marc A. Smith, Lee Rainie, Ben Shneiderman, and Camila Espina. "Classifying Twitter Topic-Networks Using Social Network Analysis." Social Media + Society 3, no. 1 (January 2017): 205630511769154. http://dx.doi.org/10.1177/2056305117691545.

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As users interact via social media spaces, like Twitter, they form connections that emerge into complex social network structures. These connections are indicators of content sharing, and network structures reflect patterns of information flow. This article proposes a conceptual and practical model for the classification of topical Twitter networks, based on their network-level structures. As current literature focuses on the classification of users to key positions, this study utilizes the overall network structures in order to classify Twitter conversation based on their patterns of information flow. Four network-level metrics, which have established as indicators of information flow characteristics—density, modularity, centralization, and the fraction of isolated users—are utilized in a three-step classification model. This process led us to suggest six structures of information flow: divided, unified, fragmented, clustered, in and out hub-and-spoke networks. We demonstrate the value of these network structures by segmenting 60 Twitter topical social media network datasets into these six distinct patterns of collective connections, illustrating how different topics of conversations exhibit different patterns of information flow. We discuss conceptual and practical implications for each structure.
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Dissertations / Theses on the topic "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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Books on the topic "Social Networks Analysis"

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C, Freeman Linton, ed. Social network analysis. Los Angeles: SAGE, 2008.

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C, Freeman Linton, ed. Social network analysis. Los Angeles: SAGE, 2008.

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Knoke, David. Social network analysis. 2nd ed. Los Angeles: Sage Publications, 2008.

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Missaoui, Rokia, Sergei O. Kuznetsov, and Sergei Obiedkov, eds. Formal Concept Analysis of Social Networks. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64167-6.

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Gündüz-Öğüdücü, Şule, and A. Şima Etaner-Uyar, eds. Social Networks: Analysis and Case Studies. Vienna: Springer Vienna, 2014. http://dx.doi.org/10.1007/978-3-7091-1797-2.

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Falkowski, Tanja. Community analysis in dynamic social networks. Go ttingen: Sierke, 2009.

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Tsvetovat, Maksim. Social network analysis for startups. Beijing: O'Reilly, 2011.

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M, Durland Maryann, Fredericks Kimberly A, and American Evaluation Association, eds. Social network analysis in program evaluation. San Francisco, Calif: Jossey-Bass, 2005.

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Disrupting criminal networks: Network analysis in crime prevention. Boulder: FirstForumPress, Inc., 2015.

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C, Freeman Linton, White Douglas R, Romney A. Kimball, and University of California, Irvine. Research Program in Social Network Analysis., eds. Research methods in social network analysis. Fairfax, Va: George Mason University Press, 1989.

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Book chapters on the topic "Social Networks Analysis"

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Aggrawal, Niyati, and Adarsh Anand. "Link Analysis." In Social Networks, 67–82. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003088066-5.

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Aggrawal, Niyati, and Adarsh Anand. "Social Network Analysis Tools." In Social Networks, 205–32. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003088066-12.

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Zhu, Mengxiao. "Social Networks Analysis." In Methodology of Educational Measurement and Assessment, 231–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74394-9_13.

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Tang, Jie, and Juanzi Li. "Social Tie Analysis." In Semantic Mining of Social Networks, 11–68. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-031-79462-9_2.

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Tang, Jie, and Juanzi Li. "Social Influence Analysis." In Semantic Mining of Social Networks, 69–110. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-031-79462-9_3.

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Thovex, Christophe, Bénédicte LeGrand, Ofelia Cervantes, J. Alfredo Sánchez, and Francky Trichet. "Semantic Social Networks Analysis." In Encyclopedia of Social Network Analysis and Mining, 1–12. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_381-1.

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Thovex, Christophe, Francky Trichet, and Bénédicte LeGrand. "Semantic Social Networks Analysis." In Encyclopedia of Social Network Analysis and Mining, 1659–69. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_381.

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Thovex, Christophe, Bénédicte LeGrand, Ofelia Cervantes, J. Alfredo Sánchez, and Francky Trichet. "Semantic Social Networks Analysis." In Encyclopedia of Social Network Analysis and Mining, 2356–67. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_381.

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Bródka, Piotr, and Przemyslaw Kazienko. "Multilayer Social Networks." In Encyclopedia of Social Network Analysis and Mining, 1–15. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_239-1.

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Gloor, Peter, and Jana Diesner. "Semantic Social Networks." In Encyclopedia of Social Network Analysis and Mining, 1–6. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4614-7163-9_90-1.

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Conference papers on the topic "Social Networks Analysis"

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Ben-Zvi, Tal. "Social networks analysis." In the Behavioral and Quantitative Game Theory. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1807406.1807490.

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Shahriari, Mohsen, and Ralf Klamma. "Signed Social Networks." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808797.2809357.

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Abufouda, Mohammed, and Katharina Anna Zweig. "Interactions around social networks matter: Predicting the social network from associated interaction networks." In 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2014. http://dx.doi.org/10.1109/asonam.2014.6921574.

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Melo, Renato Silva, and André Luís Vignatti. "Preprocessing Rules for Target Set Selection in Complex Networks." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/brasnam.2020.11167.

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In the Target Set Selection (TSS) problem, we want to find the minimum set of individuals in a network to spread information across the entire network. This problem is NP-hard, so find good strategies to deal with it, even for a particular case, is something of interest. We introduce preprocessing rules that allow reducing the size of the input without losing the optimality of the solution when the input graph is a complex network. Such type of network has a set of topological properties that commonly occurs in graphs that model real systems. We present computational experiments with real-world complex networks and synthetic power law graphs. Our strategies do particularly well on graphs with power law degree distribution, such as several real-world complex networks. Such rules provide a notable reduction in the size of the problem and, consequently, gains in scalability.
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Chiu, Terry Hui-Ye, and ShyMin Chen. "Propagating online social networks." In ASONAM '13: Advances in Social Networks Analysis and Mining 2013. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2492517.2500277.

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Tabassum, Shazia. "Social Network Analysis of Mobile Streaming Networks." In 2016 17th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2016. http://dx.doi.org/10.1109/mdm.2016.84.

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Long, Feiyu, Nianwen Ning, Chenguang Song, and Bin Wu. "Strengthening social networks analysis by networks fusion." In ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3341161.3342939.

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Tsai, Cheng-Hung, Han-Wen Liu, Ping-Che Yang, Tsun Ku, and Wu-Fan Chien. "Social persona preference analysis on social networks." In 2015 International Conference on Connected Vehicles and Expo (ICCVE). IEEE, 2015. http://dx.doi.org/10.1109/iccve.2015.10.

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Cognini, Riccardo, Damiano Falcioni, and Alberto Polzonetti. "Social networks: Analysis for integrated social profiles." In 2015 Internet Technologies and Applications (ITA). IEEE, 2015. http://dx.doi.org/10.1109/itecha.2015.7317372.

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Silva, Mariana O., Gabriel P. Oliveira, and Mirella M. Moro. "Analyzing Character Networks in Portuguese-language Literary Works." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/brasnam.2023.230585.

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Literary works are complex narratives with multifaceted character relationships. Studying these relationships can reveal important insights into the story’s structure and each character’s contribution to the plot development. This research investigates character networks in Portuguese-language literature using two main analytical approaches: structural network analysis and character importance metrics. Our analyses emphasize the significance of character networks in understanding the narrative structure of literary works and reveal the intricate interplay between characters in Portuguese-language literature. These findings deepen our comprehension of literary works’ fundamental structure and the characters’ pivotal role in shaping the story.
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Reports on the topic "Social Networks Analysis"

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Santos, Eunice E. Social Networks Analysis: Classification, Evaluation, and Methodologies. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada567185.

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Bonnett, Michaela, Chimdi Ezeigwe, Meaghan Kennedy, and Teri Garstka. Using Social Network Analysis to Link Community Health and Network Strength. Orange Sparkle Ball, July 2023. http://dx.doi.org/10.61152/scsf6662.

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Social network analysis (SNA) is a technique used to analyze social networks, whether it be composed of people, organizations, physical locations, or objects. It is being increasingly applied across a variety of sectors to gain insight into patterns of behavior and connectivity, the flow of information and behaviors, and to track and predict the effectiveness of interventions or programs. A key area associated with network strength using SNA is the health and wellness of individuals and communities. Both network strength and health and wellness are measured in many ways, which can obfuscate the association, so more consistency and further research is required. Despite this, the existing research using SNA to link characteristics of social networks to health and wellness find that stronger, more connected networks tend to be associated with better health outcomes. These results also present opportunities and insights for effective program implementation in response to disasters, to increase resilience, and to improve outcomes for individuals and communities.
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Bader, Brett William, Richard A. Harshman, and Tamara Gibson Kolda. Temporal analysis of social networks using three-way DEDICOM. Office of Scientific and Technical Information (OSTI), June 2006. http://dx.doi.org/10.2172/887253.

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Butenko, Sergiy. Optimization Techniques for Analysis of Biological and Social Networks. Fort Belvoir, VA: Defense Technical Information Center, March 2012. http://dx.doi.org/10.21236/ada567067.

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Johnson, Eric M., and Robert Chew. Social Network Analysis Methods for International Development. RTI Press, May 2021. http://dx.doi.org/10.3768/rtipress.2021.rb.0026.2105.

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Social Network Analysis (SNA) is a promising yet underutilized tool in the international development field. SNA entails collecting and analyzing data to characterize and visualize social networks, where nodes represent network members and edges connecting nodes represent relationships or exchanges among them. SNA can help both researchers and practitioners understand the social, political, and economic relational dynamics at the heart of international development programming. It can inform program design, monitoring, and evaluation to answer questions related to where people get information; with whom goods and services are exchanged; who people value, trust, or respect; who has power and influence and who is excluded; and how these dynamics change over time. This brief advances the case for use of SNA in international development, outlines general approaches, and discusses two recently conducted case studies that illustrate its potential. It concludes with recommendations for how to increase SNA use in international development.
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Parra, P., AJ Gordo, and SA D’Antonio. Social research applied to social networks. A methodological innovation for the analysis of Facebook Likes. Revista Latina de Comunicación Social, RLCS, February 2014. http://dx.doi.org/10.4185/rlcs-2014-1008en.

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Arely Donis, C., and TG Martín Casado. Representation of the Other in social advertising: Analysis of the graphic advertising of NGDOs in social networks. Revista Latina de Comunicación Social, March 2017. http://dx.doi.org/10.4185/rlcs-2017-1172en.

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Pietrobelli, Carlo, and Elisa Giuliani. Social Network Analysis Methodologies for the Evaluation of Cluster Development Programs. Inter-American Development Bank, November 2011. http://dx.doi.org/10.18235/0008963.

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Cluster development programs (CDPs) have been adopted widely in many countries worldwide. Many such programs aim to promote economic development by forming and strengthening inter-organizational networks. Despite their widespread diffusion, we know very little about CDP outputs or the impact CDPs have on host regions and their populations. Evaluation studies are beginning to appear, but the overall concern is that a distinct evaluation concept and method with a focus on CDPs is not yet available. The objective of this paper is to address this limitation, by proposing a novel methodological approach in the evaluation of CDPs based on the application of concepts and methods of social network analysis (SNA).
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Kruger, Diana, Marcelo Ochoa, Dante Contreras, and Daniela Zapata. The Role of Social Networks in the Economic Opportunities of Bolivian Women. Inter-American Development Bank, October 2007. http://dx.doi.org/10.18235/0011264.

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This paper explores the role of social networks in determining the participation of Bolivian women in income-generating activities. The empirical analysis intends to explore the impact of this new social variable on the economic choices of women and its relative importance with respect to other individual characteristics, such as education or number of children in the household. The empirical framework defines social network as the average outcome of people living in the same neighborhood. Estimation results suggest that social networks are an effective channel through which women obtain access to salaried jobs, which are of higher quality than jobs as self-employers. In contrast, their male counterparts find a positive but statistically insignificant effect from social networks. When considering the sex of the contact, it is found that women in urban areas benefit from other women being employed, while in rural areas women benefit from the presence of more employed male workers.
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Aberman, Noora-Lisa, Loty Diop, and Roosmarijn Verstraeten. Analysis of nutrition research networks in West Africa: Application of social network analysis to co-authorship data to understand and enhance collaboration. Washington, DC: International Food Policy Research Institute, 2021. http://dx.doi.org/10.2499/p15738coll2.134587.

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