Abstract
Community Detection is a prominent task in social network analysis. Community detection helps us to determine users with common interests or to find a set of similar people based on point of interest. The community detection is useful in many applications such as: recommendation systems, viral marketing, stock market prediction, influential user identification, finding domain expert and many more. In this paper, we provide a comprehensive survey on recent methodologies for overlapping community detection that are based on various factors viz: topological features, accuracy, density, centrality, betweenness, computational speed, modularity and Normalized Mutual Information (NMI). The detailed description of the challenges being addressed by various researchers is presented. The comparative analysis of overlapping community detection methods is also reported.
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