Social network analysis software
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Social network analysis software (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.
Networks can consist of anything from families, 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. 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. 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. 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.
Some SNA software can perform predictive analysis. 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) 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. OrgAnalytix is at the forefront of this evolution, using machine learning models to predict and analyze networks, typically for large companies employing teams of knowledge workers.
Network analysis software generally consists of either packages based on graphical user interfaces (GUIs), or packages built for scripting/programming languages. In general, the GUI packages are easier to learn, while scripting tools are more powerful and extensible. Widely used and well-documented GUI packages include NetMiner, UCINet, Pajek (freeware), GUESS, ORA, Cytoscape, Gephi, SocNetV (free software) and muxViz (opensource). Private GUI packages directed at business customers include: Orgnet, which provides training on the use of its software, Polinode, Keyhubs, KeyLines, KXEN, Keynetiq and Linkurious. Other SNA platforms, such as Idiro SNA Plus, have been specifically developed for particular industries such as telecoms and online gaming where massive data sets need to be analyzed.
Commonly used and well-documented scripting tools used for network analysis include: NetMiner with Python scripting engine, the statnet suite of packages for the R statistical programming language, igraph, which has packages for R and Python, muxViz (based on R statistical programming language and GNU Octave) for the analysis and the visualization of multilayer networks, the NetworkX library for Python, and the SNAP package for large-scale network analysis in C++ and Python. Though difficult to learn, some of these open source packages are growing much faster in terms of functionality and features than privately maintained software, and extensive documentation and tutorials are available.
Visual representations of social networks are important to understand network data and convey the result of the analysis. 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. All of the tools above contain visualization capabilities. NetMiner, igraph, Cytoscape, muxViz and NetworkX have the highest level of functionality in terms of producing high-quality graphics.
Interactive Data Visualization technology often includes social network analysis capabilities. In this technology, other forms of data visualization are used to interact with social network graphs. These forms of visualization include a variety of charting visualizations, tables, time lines and maps and the ability to display data in any of these forms while also applying functions to explore the data in an interactive user experience. For example, complex social network graphs can be filtered using summary chart visualizations or timelines to isolate portions of the social network graph that are of interest to the analyst. Interactive Data Visualization may also include the ability to integrate data and publish dashboards or templates to report results.
|Product||Main Functionality||Input Format||Output Format||Platform||License and cost||Notes|
|AllegroGraph||Graph Database. RDF with Gruff visualization tool||RDF||RDF||Linux, Mac, Windows||Free and Commercial||AllegroGraph is a graph database. It is disk-based, fully transactional OLTP database that stores data structured in graphs rather than in tables. AllegroGraph includes a Social Networking Analytics library.|
|Gephi||Graph exploration and manipulation software||GraphViz(.dot), Graphlet(.gml), GUESS(.gdf), LEDA(.gml), NetworkX(.graphml, .net), NodeXL(.graphml, .net), Pajek(.net, .gml), Sonivis(.graphml), Tulip(.tlp, .dot), UCINET(.dl), yEd(.gml), Gephi (.gexf), Edge list(.csv), databases||GUESS(.gdf), Gephi(.gexf), .svg, .png||Any system supporting Java 1.6 and OpenGL||Open Source (GPL3), seeking contributors||Gephi 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.|
|GraphStream||Dynamic Graph Library||GraphStream(.dgs), GraphViz(.dot), Graphlet(.gml), edge list||GraphStream(.dgs), GraphViz(.dot), Graphlet(.gml), image sequence||Any system supporting Java||Open Source||With GraphStream you deal with graphs. Static and Dynamic.
You create them from scratch, from a file or any source. You display and render them.
|Graph-tool||Python module for efficient analysis and visualization of graphs.||GraphViz(.dot), GraphML||GraphViz(.dot), GraphML and multiple image formats.||GNU/Linux, Mac||Free Software (GPL3)||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.|
|Graphviz||Graph vizualisation software||GraphViz(.dot)||Multiple image formats.||Linux, Mac, Windows||Open Source (CPL)||Graphviz is open source graph visualization framework. It has several main graph layout programs suitable for social network visualization.|
|InfiniteGraph||Highly scalable, distributed Graph Database.||SNAP, Gremlin, formatted text files for high speed, parallel loading||Gremlin, plus user definable||Linux, Mac, Windows||Commercial||InfiniteGraph is a distributed graph database that can exploit mixed disk, SSD and in-memory algorithms. It is built on a specialized distributed database engine that is optimized for storing objects with large numbers of connections. It supports parallel queries, path definition and link hunting. There is a sample Social Networking Analytics application.|
|Java Universal Network/Graph (JUNG) Framework||network and graph manipulation, analysis, and visualization||built-in support for GraphML, Pajek, and some text formats; user can create parsers for any desired format||built-in support for GraphML, Pajek, and some text formats; user can create exporters for any desired format||Any platform supporting Java||Open source (BSD license)||JUNG is a Java API and library that provides a common and extensible language for the modeling, analysis, and visualization of relational data. It supports a variety of graph types (including hypergraphs), supports graph elements of any type and with any properties, enables customizable visualizations, and includes algorithms from graph theory, data mining, and social network analysis (e.g., clustering, decomposition, optimization, random graph generation, statistical analysis, distances, flows, and centrality (PageRank, HITS, etc.)). It is limited only by the amount of memory allocated to Java.|
|Mathematica||Graph analysis, statistics, data visualization, optimization, image recognition.||CSV, DOT, GraphML, JSON, Pajek, XLS and multiple other non-network formats.||CSV, DOT, GraphML, JSON, Pajek, XLS and multiple other non-network formats.||Windows, Macintosh, Linux||Commercial||Mathematica is a general purpose computation and analysis environment.|
|Network Overview Discovery Exploration for Excel (NodeXL)||Network overview, discovery and exploration||email, .csv (text), .txt, .xls (Excel), .xslt (Excel 2007, 2010, 2013), .net (Pajek), .dl (UCINet), GraphML||.csv (text), .txt, .xls (Excel), .xslt (Excel 2007), .dl (UCINet), GraphML||Windows XP/Vista/7||Free (Ms-PL)||NodeXL is a free and open Excel 2007, 2010, 2013 Add-in and C#/.Net library for network analysis and visualization. It integrates into Excel 2007, 2010, 2013 and adds directed graph as a chart type to the spreadsheet and calculates a core set of network metrics and scores. Supports extracting email, Twitter, YouTube, Facebook, WWW, 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 4||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)||Microsoft Windows||Free(Coursework)
|NetMiner 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.
|NetworkX||Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.||GML, Graph6/Sparse6, GraphML, GraphViz (.dot), NetworkX (.yaml, adjacency lists, and edge lists), Pajek (.net), LEDA||GML, Gnome Dia, Graph6/Sparse6, GraphML, GraphViz (.dot), NetworkX (.yaml, adjacency lists, and edge lists), Pajek (.net), and assorted image formats (.jpg, .png, .ps, .svg, et al.)||Open source (GPL and similar)||Free||NetworkX (NX) is a toolset for graph creation, manipulation, analysis, and visualization. User interface is through scripting/command-line provided by Python. NX includes a several algorithms, metrics and graph generators. Visualization is provided through pylab and graphviz.
NX is an open-source project, in active development since 2004 with an open bug-tracking site, and user forums. Development is sponsored by Los Alamos National Lab.
|R||Social network analysis within the versatile and popular R environment||R will read in almost any format data file||R has write capability for most data formats||Windows, Linux, Mac||Open source||R contains several packages relevant for social network analysis:
|Tulip||Social Network Analysis tool||Tulip format (.tlp), GraphViz (.dot), GML, txt, adjacency matrix||.tlp, .gml||Windows Vista, XP, 7/ Linux / Mac OS||LGPL||Tulip 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.|
|UNISoN (Social Network Analysis Tool)||Download usenet messages and save SNA output files||Reads from free NNTP servers||Creates CSV files and Pajek .net files||Any system supporting Java||Open Source||A java application that can download Usenet messages from free NNTP servers, show the saved messages, then allow filtering of data to save to a Pajek network file or CSV file. It creates networks using the author of each post. If someone replies to a post, there is a unidirectional link created from the author of the post to the author of the message they are replying to. There is also a preview panel that shows the network visually.|
|Wolfram Alpha||Graph analysis, time series analysis, categorical data analysis||Facebook API||Many formats||Web service||Free||Wolfram Alpha is a general computational knowledge engine answering queries on many knowledge domains. Give it the input "Facebook report" and it will answer queries on analysis of your social network data,|
- Comparison of research networking tools and research profiling systems
- Social network
- Social network analysis
- Social networking
- J. F. Padgett, "Robust Action and the Rise of the Medici, 1400–1434" American Journal of Sociology, 1993 
- Wasserman & Faust, Social Network Analysis Methods and Applications
- Robert Hanneman (1998-10-20). "Introduction to Social Network Methods: Table of Contents". Faculty.ucr.edu. Retrieved 2012-10-24.
- "Introduction to Social Network Methods: Chapter 1: Social Network Data". Faculty.ucr.edu. Retrieved 2012-10-24.
- "Only connect: Felix Grant looks at the application of data analysis software to social networks", Scientific Computing World June 2010: pp 9–10.
- "Homophily". Analytictech.com. Retrieved 2012-10-24.
- "MuxViz: a tool for multilayer analysis and visualization of networks", Journal of Complex Networks Vol. 3, 159.
- "Social Network Analysis in R". Sna.stanford.edu. Retrieved 2012-10-24.
- "JoSS: Journal of Social Structure". Cmu.edu. Retrieved 2012-10-24.
- Bastian, M., Heymann, S., & Jacomy, M. (2009, May). Gephi: an open source software for exploring and manipulating networks. In ICWSM (pp. 361-362).
- Facebook friends mapped by Wolfram Alpha app BBC News
- Wolfram Alpha Launches Personal Analytics Reports For Facebook Tech Crunch
- 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". Journal of Economic Theory. 71: 44–74. doi:10.1006/jeth.1996.0108. 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.
- 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.  via JSTOR
- 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. doi:10.1137/S003614450342480.
- 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. (2003). Small Worlds: The Dynamics of Networks between Order and Randomness. Princeton: Princeton University Press.
- 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 Cyber-Place: Changing Portals and the Rise of Networked Individualism." International Journal for Urban and Regional Research 25 (2): 227-52.
- 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 Interactive Telecommunications Program, Tisch School of the Arts/ New York University
- 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.