Relevant bibliographies by topics / Social Network Visualization

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

Academic literature on the topic 'Social Network Visualization'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Social Network Visualization"

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Sonderegger, Gabi, C. Oberlack, J.C. Llopis, PH Verburg, and A. Heinimann. "Telecoupling visualizations through a network lens: a systematic review." Ecology and Society 25, no. 4 (2021): 47. https://doi.org/10.5751/ES-11830-250447.

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Telecoupling is an integrative social-ecological framework that has made important contributions to understanding land change processes in a hyperconnected world. Visualizations are a powerful tool to communicate knowledge about telecoupling phenomena. However, little is known about current practices of telecoupling visualization and the challenges involved in visually displaying connections between multiple social-ecological systems. Our research takes stock of existing telecoupling visualizations and provides recommendations for improving current practices. We systematically review 118 visua
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Jason, Leonard A., Sikora AJ, Bobak T, and Wu D. "Visualization of Social Networks in a Recovery Home." Psychology & Psychological Research International Journal 9, no. 3 (2024): 1–4. http://dx.doi.org/10.23880/pprij-16000422.

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This study utilized the SNA and NDTV packages in R to model the social network behaviors of residents within an Oxford House recovery home, where one of the entering residents was on Medication for Opioid Use Disorder (MOUD). The social network dynamics of this person on MOUD over time showed positive transformative connections among residents for friendship, advice-seeking, and lending resources, as well as increases in recovery capital. We provide the script, code, and data to allow for replication and adaptation to other studies involving social networks.
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Gemici, Kurtuluş, and Anthony Vashevko. "Visualizing Hierarchical Social Networks." Socius: Sociological Research for a Dynamic World 4 (January 1, 2018): 237802311877298. http://dx.doi.org/10.1177/2378023118772982.

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The authors propose a novel technique for the visualization of networks that contain a hierarchical structure: networks in which certain nodes and groups of nodes can be classified through a relation of precedence. Networks with a hierarchical structure frequently arise in sociology and various other disciplines, but the existing methods for visualizing such networks leave much to be desired. The method developed in this work builds on the tradition of visualization in social network analysis; it aims to simultaneously represent the positions of different nodes and the relationships between gr
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Mayer, Katja. "Objectifying Social Structures. Network visualization as means of social optimization." Theory and Psychology 22, no. 2 (2012): 162–78. https://doi.org/10.5281/zenodo.49591.

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Social network analysis offers a broad range of formal and interpretative methodologies to deal with social structures, not only by discursive, but also by visual means. Sociograms, depicting social relations as nodes and lines, have played an important part in the reification of social structures since the beginnings of sociometry. This paper brings together two strands of analysis: first, a historical perspective on the development of social network visualization; and, second, exemplary stories of black-boxed technologies that inform not only the depiction, but also the interpretation of soc
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Brandes, Ulrik, Patrick Kenis, and Jörg Raab. "Explanation Through Network Visualization." Methodology 2, no. 1 (2006): 16–23. http://dx.doi.org/10.1027/1614-2241.2.1.16.

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Assessments of configurations, dynamics, and cause and effect are at the heart of our thinking and explanation. Although numerous methods for such assessments have been developed and are being used in daily scientific practice, visualization is usually not considered to be one of them. In this article we first argue that this is due to the common practice of visualizing data rather than the information contained in it; second, we address a number of principles for effective visualization; and third, we assess visualizations generated by the software tool visone in order to explain network outc
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Bingenheimer, M., J. J. Hung, and S. Wiles. "Social network visualization from TEI data." Literary and Linguistic Computing 26, no. 3 (2011): 271–78. http://dx.doi.org/10.1093/llc/fqr020.

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Xiang, Zhang. "Visual Dynamic Simulation Model of Unstructured Data in Social Networks." Security and Communication Networks 2022 (January 13, 2022): 1–10. http://dx.doi.org/10.1155/2022/9095330.

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Social networks contain a large amount of unstructured data. To ensure the stability of unstructured big data, this study proposes a method for visual dynamic simulation model of unstructured data in social networks. This study uses the Hadoop platform and data visualization technology to establish a univariate linear regression model according to the time correlation between data, estimates and approximates perceptual data, and collects unstructured data of social networks. Then, the unstructured data collected from the original social network are processed, and an adaptive threshold is desig
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Lestari, Diana Wahyu, and Rita Ambarwati. "Social Network Analysis: Understanding User Behavior in Threads." Kontigensi : Jurnal Ilmiah Manajemen 12, no. 1 (2024): 115–24. http://dx.doi.org/10.56457/jimk.v12i1.505.

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The presence of threads has enhanced users' ability to share stories, information and views in greater detail and effectively in the online world. Social media platforms allow users to interact, share content, and connect online. Therefore, the purpose of this research is to create a Social Network Analysis (SNA) network visualization and categorize the data to contribute to the understanding of user interaction on this platform. The research method in this study is the Social Network Analysis (SNA) approach to analyze the relationship between individuals in social networks. The results show t
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Akula, Ramya, and Ivan Garibay. "VizTract: Visualization of Complex Social Networks for Easy User Perception." Big Data and Cognitive Computing 3, no. 1 (2019): 17. http://dx.doi.org/10.3390/bdcc3010017.

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Social networking platforms connect people from all around the world. Because of their user-friendliness and easy accessibility, their traffic is increasing drastically. Such active participation has caught the attention of many research groups that are focusing on understanding human behavior to study the dynamics of these social networks. Oftentimes, perceiving these networks is hard, mainly due to either the large size of the data involved or the ineffective use of visualization strategies. This work introduces VizTract to ease the visual perception of complex social networks. VizTract is a
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Brandes, Ulrik. "Social network analysis and visualization [Applications Corner]." IEEE Signal Processing Magazine 25, no. 6 (2008): 147–51. http://dx.doi.org/10.1109/msp.2008.929814.

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Dissertations / Theses on the topic "Social Network Visualization"

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Köstinger, Harald. "ViNCent – Visualization of NetworkCentralities." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-10793.

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In the area of information visualization social or biological networks are visualized ina way so that they can be explored easily and one can get more information about thestructure of the network out of it. The use of network centralities in the field of network analysis plays an importantrole when it comes to the rating of the relative importance of vertices within the networkstructure based on the neighborhood of them. Such a single network can be renderedeasily by the use of standard graph drawing algorithms. But it is not only the explorationof one centrality which is important. Furthermo
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Yu, En. "Social Network Analysis Applied to Ontology 3D Visualization." Miami University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=miami1206497854.

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Eiesland, Jon Wostryck. "Communities in a large social network : visualization and analysis." Thesis, Norwegian University of Science and Technology, Department of Physics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-6409.

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<p>Communities have been a hot topic in complex network research the last years. Several algorithms for detecting communities have been developed, and in this thesis we use the sequential clique percolation algorithm to detect communities in a large social network. Our network consists of 5.3 million mobile phone users, with mutual communication data aggregated over 18 weeks.</p><p>In this thesis we do a visual study of the communities, and we clearly see the nested community structure when we do clique percolation for dierent clique sizes. When we threshold the edge weights we see that the st
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Pei, Wanjun. "3D Visualization of Heterogeneous User Interactions in a Social Network." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32472.

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With the popularity of social networks, users can communicate with each other in a more convenient way. However, the increasing amount of data poses new challenges for the analysis of the social activities of the users. In this article, we propose to visualize the heterogeneous information of user interactions in a social network in a three-dimensional way using the concept of solar systems. The target user represents the center of the solar system. We determine physical variables from interaction frequencies between the user and their friends to be visualized. This gives the user a better ins
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Perer, Adam Nathaniel. "Integrating statistics and visualization to improve exploratory social network analysis." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8502.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.<br>Thesis research directed by: Dept. of 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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Fabbri, Renato. "Topological stability and textual differentiation in human interaction networks: statistical analysis, visualization and linked data." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-11092017-154706/.

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This work reports on stable (or invariant) topological properties and textual differentiation in human interaction networks, with benchmarks derived from public email lists. Activity along time and topology were observed in snapshots in a timeline, and at different scales. Our analysis shows that activity is practically the same for all networks across timescales ranging from seconds to months. The principal components of the participants in the topological metrics space remain practically unchanged as different sets of messages are considered. The activity of participants follows the expected
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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 social
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Morell, David James Jr. "Visualizing and Characterizing Real World Events on Twitter." Miami University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=miami1340734887.

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Maushagen, Jan. "Visual Analysis of Publication Networks." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-27487.

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This thesis documents the development of a web-application attacking the problem of visualization of co-authorship networks. The visualization encompasses several views.Each of them shows different aspects of the data which is loaded from Academic Archive Online (DiVa), a library system which holds all publications released in the Linnaeus University.  To detect relationships among authors, a new interactive layout for Node-Link Diagrams was developed which shows publications, authors and corresponding organizations (faculties, departments) in a radial manner. This Network-View is connected to
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Amento, Brian. "User Interfaces for Topic Management of Web Sites." Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/29871.

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Topic management is the task of gathering, evaluating, organizing, and sharing a set of web sites for a specific topic. Current web tools do not provide adequate support for this task. We created and continue to develop the TopicShop system to address this need. TopicShop includes (1) a web crawler/analyzer that discovers relevant web sites and builds site profiles, and (2) user interfaces for information workspaces. We conducted an empirical pilot study comparing user performance with TopicShop vs. Yahooï . Results from this study were used to improve the design of TopicShop. A number of
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Books on the topic "Social Network Visualization"

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Abraham, Ajith. Computational Social Networks: Mining and Visualization. Springer London, 2012.

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Blanuca, Viktor, Leonid Bezrukov, Egor Sherin, and Anatoliy Yakobson. Public geography: Digital priorities of the XXI century. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1863096.

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Social geography, aimed at understanding the territorial organization of society, has a number of digital priorities that have not been analyzed before. They are related to the objects and methods of research, as well as to the methods of visualization and the areas of application of the results obtained.&#x0D; The monograph analyzes the world experience of socio-geographical study of platform economy, telecommunication networks and "smart cities", the use of data mining, models of spatial diffusion of innovations and zoning, visualization through cartographic image, dendrogram and tag cloud,
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Means, Garann. Node for Front-End Developers. O'Reilly Media, 2012.

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Mastering Gephi Network Visualization. Packt Publishing, 2015.

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Social Networking: Mining, Visualization, and Security. Springer, 2014.

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Dehuri, Satchidananda, Mrutyunjaya Panda, and Gi-Nam Wang. Social Networking: Mining, Visualization, and Security. Springer London, Limited, 2014.

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Dehuri, Satchidananda, Mrutyunjaya Panda, and Gi-Nam Wang. Social Networking: Mining, Visualization, and Security. Springer, 2016.

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Doreian, P. P. Understanding Large Temporal Networks and Spatial Networks - Exploration, Pattern Searching, Visualization and Network Evolution. Wiley & Sons, Limited, John, 2014.

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Magdy, Mohammed. Mastering Gephi Network Visualization: Produce Advanced Network Graphs in Gephi and Gain Valuable Insights into Your Network Datasets. Packt Publishing, Limited, 2015.

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Doreian, Patrick, Vladimir Batagelj, Anuska Ferligoj, and Natasa Kejzar. Understanding Large Temporal Networks and Spatial Networks: Exploration, Pattern Searching, Visualization and Network Evolution. Wiley & Sons, Incorporated, John, 2014.

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Book chapters on the topic "Social Network Visualization"

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Muelder, Chris, Liang Gou, Kwan-Liu Ma, and Michelle X. Zhou. "Multivariate Social Network Visual Analytics." In Multivariate Network Visualization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06793-3_3.

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Wu, Jiang. "Visualization of Social Networks." In Social Network Computing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4084-0_2.

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Chen, Ing-Xiang, and Cheng-Zen Yang. "Visualization of Social Networks." In Handbook of Social Network Technologies and Applications. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7142-5_27.

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Pfeffer, Jürgen, and Linton C. Freeman. "Social Network Visualization, Methods of." In Encyclopedia of Complexity and Systems Science. Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-642-27737-5_496-2.

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Freeman, Linton C. "Social Network Visualization, Methods of." In Computational Complexity. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1800-9_184.

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Freeman, Linton C. "Social Network Visualization, Methods of." In Encyclopedia of Complexity and Systems Science. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_496.

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Stein, Klaus, René Wegener, and Christoph Schlieder. "Pixel-Oriented Network Visualization: Static Visualization of Change in Social Networks." In Lecture Notes in Social Networks. Springer Vienna, 2012. http://dx.doi.org/10.1007/978-3-7091-1346-2_5.

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Hu, Yifan. "Visualization of Large Networks." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_44-1.

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Hu, Yifan. "Visualization of Large Networks." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_44.

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Hu, Yifan. "Visualization of Large Networks." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_44.

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Conference papers on the topic "Social Network Visualization"

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Praneeth, Chunduri, RM Noorullah, K. Hitesh Karthik, and P. Jopesh Rupak. "Visualization and Analysis of Social Network-Based Diverse Datasets Using Multi-Viewpoint Similarity Metrics." In 2024 International Conference on Expert Clouds and Applications (ICOECA). IEEE, 2024. http://dx.doi.org/10.1109/icoeca62351.2024.00046.

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Wu, Peng, and SiKun Li. "Social Network Visualization via Domain Ontology." In 2009 International Conference on Information Engineering and Computer Science. ICIECS 2009. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5362898.

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Du, Jiang, Yafei Xian, and Jiayu Yang. "A Survey on Social Network Visualization." In 2015-1st International Symposium on Social Science. Atlantis Press, 2015. http://dx.doi.org/10.2991/isss-15.2015.101.

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Abdur Rahman, Md, Abdulmotaleb El Saddik, and Wail Gueaieb. "Data visualization: From body sensor network to social networks." In 2009 IEEE International Workshop on Robotic and Sensors Environments (ROSE). IEEE, 2009. http://dx.doi.org/10.1109/rose.2009.5356001.

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Ibrahim, Siti Nurkhadijah Aishah, Ali Selamat, and Mohd Hafiz Selamat. "E-Business Social Network Optimization and Visualization." In 2008 Eighth International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2008. http://dx.doi.org/10.1109/isda.2008.184.

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Lee, Dong Liang, and Lawrence Y. Deng. "Social network visualization for interest in music." In 2017 International Conference on Applied System Innovation (ICASI). IEEE, 2017. http://dx.doi.org/10.1109/icasi.2017.7988412.

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"Semantic Visualization in Social Network Analysis - A Social Network Analysis Example Built using Tom Sawyer Perspectives." In International Conference on Information Visualization Theory and Applications. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004289105500553.

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Wu, Tongshuang, Yuan Yao, Yuqing Duan, Xinzhi Fan, and Huamin Qu. "NetworkSeer: Visual analysis for social network in MOOCs." In 2016 IEEE Pacific Visualization Symposium (PacificVis). IEEE, 2016. http://dx.doi.org/10.1109/pacificvis.2016.7465269.

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Kim, Yong-Il, Yoo-Kang Ji, and Sun Park. "Social Network Visualization Method using User’s Social Relationship based on Hadoop." In The 2nd International Conference on Convergence and its Application. Science & Engineering Research Support soCiety, 2015. http://dx.doi.org/10.14257/astl.2013.30.01.

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Luo, Wei, Alan M. MacEachren, Peifeng Yin, and Frank Hardisty. "Spatial-social network visualization for exploratory data analysis." In the 3rd ACM SIGSPATIAL International Workshop. ACM Press, 2011. http://dx.doi.org/10.1145/2063212.2063216.

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Reports on the topic "Social Network Visualization"

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Xu, Ran. Introduction to Social Network Analysis (Free Seminar). Instats Inc., 2024. https://doi.org/10.61700/zud3mrhp34b2l1910.

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This seminar provides an introduction to social network analysis (SNA), equipping researchers in social sciences, population health science, and systems science with essential tools to examine relationships and behaviors in varied contexts, including mHealth and social media. Attendees will gain a practical understanding of the basics of network theory, data analysis, visualization methods, and real-world applications of SNA.
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Kennedy, Meaghan, Michaela Bonnett, and Teri Garstka. A Model for Technology-Enabled Community Resilience. Orange Sparkle Ball, 2024. http://dx.doi.org/10.61152/plcr9111.

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Introduction Tech-Enabled Community Resilience is an innovative model designed to enhance resilience and optimize impact in complex systems such as communities and ecosystems. The model leverages social network analysis and technology to visualize network dynamics, measure interactions, and implement targeted interventions. Model Structure The approach consists of two key stages: a Startup Phase focused on assembling champions and co-creating a shared vision, and a Steady-state Phase involving iterative measurement and intervention. By utilizing technology platforms for data collection and vis
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