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Characterization of Online Social Network: A Case Study on Twitter Data
Abstract
The rapid growth of internet with large number of social network sites makes it easy to interconnect people from all over the world on a shared platform. Social network can be represented by a graph, where individual users are represented as nodes/vertices and connections between them are represented as edges of the graph. As social network inherits the properties of graph, its characterization includes centrality and community detection. In this chapter we discuss three centrality measures and its effects for information propagation. We discuss three popular hierarchical community detection measures and make a comparative analysis of them. Moreover we propose a new ego-based community detection algorithm which can be very efficient in terms of time complexity for very large network like online social network. In this chapter, a network is formed based on the data collected from Twitter account using hashtag(#).
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