Abstract
A social network can be defined as a complex graph, which is a collection of nodes connected via edges. Nodes represent individual actors or people in the network, whereas edges define relationships among those actors. Most popular social networks are Facebook, Twitter, and Google+. To analyze these social networks, one needs specialized tools for analysis. This chapter presents a comparative study of such tools based on the general graph aspects as well as the social network mining aspects. While considering the general graph aspects, this chapter presents a comparative study of four social network analysis tools—NetworkX, Gephi, Pajek, and IGraph—based on the platform, execution time, graph types, algorithm complexity, input file format, and graph features. On the basis of the social network mining aspects, the chapter provides a comparative study on five specialized tools—Weka, NetMiner 4, RapidMiner, KNIME, and R—with respect to the supported mining tasks, main functionality, acceptable input formats, output formats, and platform used.
Top2. Social Network Analysis
The internet has revolutionized the sharing of information through number of information sharing systems, including the Web. As of July 2015, number of internet users are 3.17 billion. Out of them, 2.3 billion users are active on social media having an average of 5.54 social media accounts. As of April 2016, Instagram has over 400 million users, and LinkedIn has over 450 million users. Facebook is said to have about 1.71 billion monthly active users, as of second quarter of 2016. These numbers significantly shows how popular social networks are.
Social network analysis (SNA) is the methodical analysis of social networks through the use of network structure and graph theories. Social network analysis views social relationships in terms of network theory, consisting of nodes (representing individual actors within the network) and ties (which represent relationships between the individuals, such as friendship, kinship, organizational position, social relationships, etc.) (Monclar, 2011).
Analysis tasks of social networks includes following:
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Discovering the structure of social network
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Finding various attribute values for the network- Ex. radius, diameter, centrality, betweenness, shortest paths, density etc.
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Finding communities in the social network
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Visualizing the whole or part of the social network