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Wednesday, January 3, 2018

SocNetV - Social Network Analysis and Visualization Software
src: socnetv.org

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.


Example of basic Social Network Analysis of Facebook friends - YouTube
src: i.ytimg.com


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 NetMiner, UCINet, Pajek (freeware), GUESS, ORA, Cytoscape, Gephi, SocNetV (free software) and muxViz (opensource).

Private GUI packages directed at business customers include: Keyhubs, KeyLines, KXEN, Keynetiq, Linkurious, OrgAnalytix, Orgnet, which provides training on the use of its software, Polinode. 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.

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. NetMiner, igraph, Cytoscape, muxViz and NetworkX have the highest level of functionality in terms of producing high-quality graphics.

Other Options

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.


Symbols : Foxy The Early Beginnings Social Network Analysis And ...
src: indexxit.com


Collection of social network analysis tools and libraries

Click the "show" link (next to "Notes") to expand the list.

The suite of innovative solutions can be custom-fit to address specific, persistent issues facing the organization. OA provides leaders with deep understanding of relationships and informal networks within their teams to enable enhanced performance, innovation, and productivity.  


IBM i2 Analyst's Notebook Social Network Analyze - YouTube
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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

Social Networks - broadstuff
src: www.theagileelephant.com


References

Notes


Social Network Visualizer: SocNetV Manual
src: socnetv.org


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