Social Network Analysis

Social Network Analysis

Paramita Dey (Government College of Engineering and Ceramic Technology, India) and Sarbani Roy (Jadavpur University, India)
Copyright: © 2016 |Pages: 29
DOI: 10.4018/978-1-4666-9964-9.ch010
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Abstract

Social Network Analysis (SNA) looks at how our world is connected. The mapping and measuring of connections and interactions between people, groups, organizations and other connected entities are very significant and have been the subject of a fascinating interdisciplinary topic . Social networks like Twitter, Facebook, LinkedIn are very large in size with millions of vertices and billions of edges. To collect meaningful information from these densely connected graph and huge volume of data, it is important to find proper topology of the network as well as analyze different network parameters. The main objective of this work is to study network characteristics commonly used to explain social structures. In this chapter, we discuss all important aspect of social networking and analyze through a real time example. This analysis shows some distinguished parameters like number of clusters, group formation, node degree distribution, identifying influential leader/seed node etc. which can be used further for feature extraction.
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In 1950 social network analysis evolved as a research subjects for the sociologists and social anthropologists. But in late 80s the techniques are gradually evolved for the analysis of data for inference of the characteristics inherent between the social networks (Wasserman, 1994). As it is easier to save the social network in the form of a graph, different topologies are evolved for the proper representation of the data. It is started with random graph and evolved to more specialise one. As the data of OSN is a huge one and the number of users increased day by day, graph sampling algorithm was evolved which is based on the idea of clustering coefficient and node degree distribution will face a very little change at the time of sampling. Wang et al (2011) elaborate this in their paper and make a comparative study of different graph sampling techniques. A large research work was done for finding the centrality measures, especially between centrality for finding important nodes (Wayne and Oellermann, 2011). Small world network, which is based on the notion of six degree separation, is now becoming an interesting research option of the researcher (Kleinberg, 1998). Now in today’s world where big data and cloud computing is the buzzword, instead of doing the graph sampling, research is moving on in the trends towards distributed computing environment.

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