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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. ==Overview== Networks can consist of anything from families,〔J. F. Padgett, "Robust Action and the Rise of the Medici, 1400–1434" American Journal of Sociology, 1993 ()〕 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.〔Wasserman & Faust, Social Network Analysis Methods and Applications〕 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.〔"Only connect: Felix Grant looks at the application of data analysis software to social networks", ''Scientific Computing World'' June 2010: pp 9–10.()〕 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. 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 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 and Keynetiq. 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,〔"MuxViz: a tool for multilayer analysis and visualization of networks", Journal of Complex Networks Vol. 3, 159.()〕 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. Also worthy of mention are the variety of tools built primarily for network visualization, some of which also contain social network analytic features. These include general purpose visualization tools such as SocNetV and Tulip; tools designed for medical applications such as SocioMetrica; tools designed for law-enforcement and intelligence organizations such as i2 Analyst's Notebook, SilentRunner Sentinel, KeyLines by Cambridge Intelligence, SVAT and Sentinel Visualizer; tools designed for corporations and businesses such as NodeXL, RapidNet, Keyhubs, Idro, Ipoint, Polinode and Sonamine. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Social network analysis software」の詳細全文を読む スポンサード リンク
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