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.
Video Social network analysis software
Overview
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.
Maps Social network analysis software
Features
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.
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.
Types of Software
Network analysis software generally consists of either packages based on graphical user interfaces (GUIs), or packages built for scripting/programming languages.
GUI Packages
In general, the GUI packages are easier to learn, while scripting tools are more powerful and extensible. Widely used, often open-sourced and well-documented GUI packages include EgoWeb 2.0 (open source), NetMiner, UCINet, Pajek (freeware), GUESS, ORA, Cytoscape, Gephi, NodeXL, Social Network Visualizer (SocNetV, free software), Meerkat (SNA), and muxViz (open source).
Private GUI packages directed at business customers include: Arcade Analytics, Idiro SNA Plus, Keyhubs, KeyLines, KXEN, Keynetiq, Linkurious, MyNetworkmap, OrgAnalytix, Orgnet, and Polinode.
Scripting/Programming Tools
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.
All of the tools above contain visualization capabilities.
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See also
- Comparison of research networking tools and research profiling systems
- Social network
- Social network analysis
- Social networking
- Organizational Network Analysis
References
Notes
External links
- International Network for Social Network Analysis (INSNA) list of software packages and libraries: Computer Programs for Social Network Analysis page.
- 2010 : A comparative study of social network analysis tools by Combe, Largeron, Egyed-Zsigmond and Géry: [6]
Source of article : Wikipedia
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