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.Ĭommonly 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, the NetworkX library for Python, and the SNAP package for large-scale network analysis in C++. Private GUI packages directed at business customers include: Orgnet, which provides training on the use of its software, Keyhubs, and KXEN. Widely used and well-documented GUI packages include NetMiner, UCINet, Pajek (freeware), GUESS, ORA, and Cytoscape. GUI packages are easier to learn, while scripting tools are more powerful and extensible. Network analysis software generally consists of either packages based on graphical user interfaces (GUIs), or packages built for scripting/programming languages. 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. Some SNA software can perform predictive analysis. 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. 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. For example, node-level features can include network phenomena such as betweeness and centrality, or individual attributes such as age, sex, or income. Network features can be at the level of individual nodes, dyads, triads, ties and/or edges, or the entire network. 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.
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