Social network analysis software

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Social network analysis (SNA) software is software which facilitates quantitative or qualitative analysis of social networks, by describing features of a network either through numerical or visual representation.

Contents

Overview

Networks can consist of anything from families, [1] project teams, classrooms, sports teams, legislatures, nation-states, disease vectors, membership on networking websites like Twitter or Facebook, or even the Internet. Networks can consist of direct linkages between nodes or indirect linkages based upon shared attributes, shared attendance at events, or common affiliations. [2] Network features can be at the level of individual nodes, dyads, triads, ties and/or edges, or the entire network. For example, node-level features can include network phenomena such as betweenness and centrality, or individual attributes such as age, sex, or income. [3] SNA software generates these features from raw network data formatted in an edgelist, adjacency list, or adjacency matrix (also called sociomatrix), often combined with (individual/node-level) attribute data. [4] Though the majority of network analysis software uses a plain text ASCII data format, some software packages contain the capability to utilize relational databases to import and/or store network features.

Features

Visual representations of social networks are important to understand network data and convey the result of the analysis. [5] Visualization often also facilitates qualitative interpretation of network data. With respect to visualization, network analysis tools are used to change the layout, colors, size and other properties of the network representation.

Some SNA software can perform predictive analysis. [6] This includes using network phenomena such as a tie to predict individual level outcomes (often called peer influence or contagion modeling), using individual-level phenomena to predict network outcomes such as the formation of a tie/edge (often called homophily models [7] ) or particular type of triad, or using network phenomena to predict other network phenomena, such as using a triad formation at time 0 to predict tie formation at time 1.

Collection of social network analysis tools and libraries

ProductMain FunctionalityInput FormatOutput FormatPlatformLicense and costNotes
Cytoscape Network analysis and visualization software.sif, .nnf, .gml, SBML, BioPAX, GraphML, Delimited text, .xls,. xlsx, Cytoscape.js JSON, Cytoscape CXCX JSON / CX2 JSON, Cytoscapre.js JSON, GraphML, PSI-MI, XGMML, SIFWindows, Linux, MacOpen sourceCytoscape is a widely used open-source platform for visualizing and analyzing complex networks. It offers a user-friendly interface, extensive plugin support, and features for data integration and advanced analysis techniques.
Gephi Graph exploration and manipulation softwareGEXF, GDF, GML, GraphML, Pajek NET, GraphViz DOT, CSV, UCINET DL, Tulip TPL, Netdraw VNA, SpreadsheetCSV, GDF, GEXF, GraphML, Pajek NET, Spreadsheet, PDF, SVGAny system supporting Java 1.6 and OpenGL Open Source (GPL3), seeking contributorsGephi [8] is an interactive visualization and exploration platform for all kinds of networks and complex systems, dynamic and hierarchical graphs. It is a tool for people that have to explore and understand graphs. The user interacts with the representation, manipulate the structures, shapes and colors to reveal hidden properties. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new possibilities to work with complex data sets and produce valuable visual results.
Graphviz Graph visualization softwareGraphViz(.dot)Multiple image formats.Windows, Linux, MacOpen Source (CPL)Graphviz is open source graph visualization framework. It has several main graph layout programs suitable for social network visualization.
Network Overview Discovery Exploration for Excel (NodeXL) Network analysis, content analysis and graph visualization softwarexlsx (Excel 2010, 2013, 2016, 2019, 2021, 365), GDF, GEXF, Pajek, UCINet, GraphMLxlsx (Excel 2010, 2013, 2016, 2019, 2021, 365), csv, GDF, GEXF, Pajek, UCINet, GraphML, NodeXL Pro INSIGHTS, PowerPointWindows 10, 11NodeXL Basic is free, NodeXL Pro is a paid subscriptionNodeXL is a (social) network analysis and visualization Add-in for Microsoft Excel written in C#. It integrates into Excel 2010, 2013, 2016, 2019, 2021, 365 and adds undirected and directed graphs as a chart type to the spreadsheet and calculates a core set of network metrics and scores. Supports data import from X (formerly Twitter), YouTube, Reddit, Wiki and Flickr social networks. Accepts edge lists and matrix representations of graphs. Allows for easy and automated manipulation and filtering of underlying data in spreadsheet format. Multiple network visualization layouts. Reads and writes Pajek, UCINet and GraphML files.
NetMiner All-in-one Software for Network Analysis and Visualization.xls(Excel),.xlsx (Excel 2007), .csv(text), .dl(UCINET), .net(Pajek), .dat(StOCNET), .gml; NMF(proprietary).xls(Excel),.xlsx (Excel 2007), .csv(text), .dl(UCINET), .net(Pajek), .dat(StOCNET), NMF(proprietary)WindowsCommercial with free trialNetMiner is a software tool for exploratory analysis and visualization of large network data. NetMiner 4 embed internal Python-based script engine which equipped with the automatic Script Generator for unskilled users. Then the users can operate NetMiner 4 with existing GUI or programmable script language.
  • Analysis of large networks(+10,000,000 nodes), comprehensive network measures and models
  • Both exploratory & confirmatory analysis
  • Interactive visual analytics
  • What-if network analysis
  • Built-in statistical procedures and charts
  • Full documentation(1,000+ pages of User's Manual)
  • Expressive network data model
  • Facilities for data & workflow management
  • Python-based Script workbench and user-friendliness
  • Morphological Analyzer for Semantic network analysis
Python Social network analysis within the versatile and popular Python environmentPython will read in almost any format data filePython has write capability for most data formatsWindows, Linux, MacOpen sourcePython contains several packages relevant for social network analysis:
  • igraph is a library collection for creating and manipulating graphs and analyzing networks. It is written in C and also exists as Python and R packages;
  • sna performs sociometric analysis of networks; network manipulates and displays network objects;
  • Networkx is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks;
  • Graph-tool is a python module for efficient analysis of graphs. Its core data structures and algorithms are implemented in C++, with heavy use of Template metaprogramming, based on the Boost Graph Library. It contains a comprehensive list of algorithms.
R Social network analysis within the versatile and popular R environmentR will read in almost any format data fileR has write capability for most data formatsWindows, Linux, MacOpen sourceR contains several packages relevant for social network analysis:
  • igraph is a library collection for creating and manipulating graphs and analyzing networks. It is written in C and also exists as Python and R packages;
  • sna performs sociometric analysis of networks;
  • network manipulates and displays network objects;
  • PAFit can analyse the evolution of complex networks by estimating preferential attachment and node fitness;
  • tnet performs analysis of weighted networks, two-mode networks, and longitudinal networks;
  • ergm is a set of tools to analyze and simulate networks based on exponential random graph models exponential random graph models;
  • Bergm provides tools for Bayesian analysis for exponential random graph models;
  • hergm implements hierarchical exponential random graph models;
  • RSiena allows the analyses of the evolution of social networks using dynamic actor-oriented models;
  • latentnet has functions for network latent position and cluster models;
  • degreenet provides tools for statistical modeling of network degree distributions;
  • networksis provides tools for simulating bipartite networks with fixed marginals;
  • multiplex offers tools for the analysis of multiple social networks with algebra;
  • migraph provides tools for analysing multimodal and multilevel networks;
  • netdiffuseR was designed for the analysis of network diffusion of innovations (and diffusion in general);
  • bipartite provides functions to visualise and calculate indices used to describe bipartite graphs. It focuses on webs, i.e., ecological networks.
Tulip Social Network Analysis toolTulip format (.tlp), GraphViz (.dot), GML, txt, adjacency matrix .tlp, .gml, GraphVis format (.dot), GML, PNG / SVG / JPEGWindows, Linux, MacOpen sourceTulip is an information visualization framework dedicated to the analysis and visualization of relational data. Tulip aims to provide the developer with a complete library, supporting the design of interactive information visualization applications for relational data that can be tailored to the problems he or she is addressing.

See also

Related Research Articles

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References

  1. ↑ Padgett, John F.; Ansell, Christopher K. (1993). "Robust Action and the Rise of the Medici, 1400-1434" (PDF). American Journal of Sociology . University of Chicago Press. 98 (6): 1259–1319. doi:10.1086/230190. ISSN   0002-9602. S2CID   56166159. Archived from the original (PDF) on 3 March 2020.
  2. ↑ Wasserman & Faust, Social Network Analysis Methods and Applications
  3. ↑ Robert Hanneman (20 October 1998). "Introduction to Social Network Methods: Table of Contents". Faculty.ucr.edu. Retrieved 24 October 2012.
  4. ↑ "Introduction to Social Network Methods: Chapter 1: Social Network Data". Faculty.ucr.edu. Retrieved 24 October 2012.
  5. ↑ "JoSS: Journal of Social Structure". Cmu.edu. Retrieved 24 October 2012.
  6. ↑ "Only connect: Felix Grant looks at the application of data analysis software to social networks", Scientific Computing World June 2010: pp 9–10.
  7. ↑ "Homophily". Analytictech.com. Retrieved 24 October 2012.
  8. ↑ Bastian, M., Heymann, S., & Jacomy, M. (2009, May). Gephi: an open source software for exploring and manipulating networks. In ICWSM (pp. 361-362).

Notes

  • Barnes, J. A. "Class and Committees in a Norwegian Island Parish", Human Relations 7:39-58
  • Borgatti, S. (2002). NetDraw Software for Network Visualization. Lexington, KY: Analytic Technologies.
  • Borgatti, S. E. (2002). Ucinet for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies.
  • Berkowitz, S. D. 1982. An Introduction to Structural Analysis: The Network Approach to Social Research. Toronto: Butterworth.
  • Brandes, Ulrik, and Thomas Erlebach (Eds.). 2005. Network Analysis: Methodological Foundations Berlin, Heidelberg: Springer-Verlag.
  • Breiger, Ronald L. 2004. "The Analysis of Social Networks." Pp. 505–526 in Handbook of Data Analysis, edited by Melissa Hardy and Alan Bryman. London: Sage Publications. Excerpts in pdf format
  • Burt, Ronald S. (1992). Structural Holes: The Structure of Competition. Cambridge, MA: Harvard University Press.
  • Carrington, Peter J., John Scott and Stanley Wasserman (Eds.). 2005. Models and Methods in Social Network Analysis. New York: Cambridge University Press.
  • Christakis, Nicholas and James H. Fowler "The Spread of Obesity in a Large Social Network Over 32 Years," New England Journal of Medicine 357 (4): 370-379 (26 July 2007)
  • Doreian, Patrick, Vladimir Batagelj, and Anuska Ferligoj. (2005). Generalized Blockmodeling. Cambridge: Cambridge University Press.
  • Freeman, Linton C. (2004) The Development of Social Network Analysis: A Study in the Sociology of Science. Vancouver: Empirical Press.
  • Hansen, William B. and Reese, Eric L. 2009. Network Genie Users Manual. Greensboro, NC: Tanglewood Research.
  • Hill, R. and Dunbar, R. 2002. "Social Network Size in Humans." Human Nature, Vol. 14, No. 1, pp. 53–72.Google
  • Jackson, Matthew O. (2003). "A Strategic Model of Social and Economic Networks" (PDF). Journal of Economic Theory. 71: 44–74. doi:10.1006/jeth.1996.0108. hdl: 10419/221454 . pdf
  • Huisman, M. and Van Duijn, M. A. J. (2005). Software for Social Network Analysis. In P J. Carrington, J. Scott, & S. Wasserman (Editors), Models and Methods in Social Network Analysis (pp. 270–316). New York: Cambridge University Press.
  • Krebs, Valdis (2002) Uncloaking Terrorist Networks, First Monday, volume 7, number 4 (Application of SNA software to terror nets Web Reference.)
  • Krebs, Valdis (2008) A Brief Introduction to Social Network Analysis (Common metrics in most SNA software Web Reference.)
  • Krebs, Valdis (2008) Various Case Studies & Projects using Social Network Analysis software Web Reference Archived 11 January 2010 at the Wayback Machine .
  • Lin, Nan, Ronald S. Burt and Karen Cook, eds. (2001). Social Capital: Theory and Research. New York: Aldine de Gruyter.
  • Mullins, Nicholas. 1973. Theories and Theory Groups in Contemporary American Sociology. New York: Harper and Row.
  • Müller-Prothmann, Tobias (2006): Leveraging Knowledge Communication for Innovation. Framework, Methods and Applications of Social Network Analysis in Research and Development, Frankfurt a. M. et al.: Peter Lang, ISBN   0-8204-9889-0.
  • Manski, Charles F. (2000). "Economic Analysis of Social Interactions". Journal of Economic Perspectives. 14 (3): 115–36. doi: 10.1257/jep.14.3.115 . JSTOR   2646922.
  • Moody, James, and Douglas R. White (2003). "Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups." American Sociological Review 68(1):103-127.
  • Newman, Mark (2003). "The Structure and Function of Complex Networks" (PDF). SIAM Review. 45 (2): 167–256. arXiv: cond-mat/0303516 . Bibcode:2003SIAMR..45..167N. doi:10.1137/S003614450342480. S2CID   221278130. Archived from the original (PDF) on 16 February 2008.
  • Nohria, Nitin and Robert Eccles (1992). Networks in Organizations. second ed. Boston: Harvard Business Press.
  • Nooy, Wouter d., A. Mrvar and Vladimir Batagelj. (2005). Exploratory Social Network Analysis with Pajek. Cambridge: Cambridge University Press.
  • Scott, John. (2000). Social Network Analysis: A Handbook. 2nd Ed. Newberry Park, CA: Sage.
  • Tilly, Charles. (2005). Identities, Boundaries, and Social Ties. Boulder, CO: Paradigm press.
  • Valente, Thomas. (1995). Network Models of the Diffusion of Innovation. Cresskill, NJ: Hampton Press.
  • Wasserman, Stanley, & Faust, Katherine. (1994). Social Networks Analysis: Methods and Applications. Cambridge: Cambridge University Press.
  • Watkins, Susan Cott. (2003). "Social Networks." Pp. 909–910 in Encyclopedia of Population. rev. ed. Edited by Paul Demeny and Geoffrey McNicoll. New York: Macmillan Reference.
  • Watts, Duncan (1999). Small worlds: the dynamics of networks between order and randomness. Princeton, N.J: Princeton University Press. ISBN   978-0-691-11704-1. OCLC   40602717.
  • Watts, Duncan. (2004). Six Degrees: The Science of a Connected Age. W. W. Norton & Company.
  • Wellman, Barry (1999). Networks in the Global Village. Boulder, CO: Westview Press.
  • Wellman, Barry (2001). "Physical Place and Cyberplace: The Rise of Personalized Networking". International Journal of Urban and Regional Research. Wiley. 25 (2): 227–252. CiteSeerX   10.1.1.169.5891 . doi:10.1111/1468-2427.00309. ISSN   0309-1317.
  • Wellman, Barry and Berkowitz, S.D. (1988). Social Structures: A Network Approach. Cambridge: Cambridge University Press.
  • Weng, M. (2007). "A Multimedia Social-Networking Community for Mobile Devices". CiteSeerX   10.1.1.538.7640 .
  • White, Harrison, Scott Boorman and Ronald Breiger. 1976. "Social Structure from Multiple Networks: I Blockmodels of Roles and Positions." American Journal of Sociology 81: 730–80.
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