An Adaptive Approximation Algorithm for Community Detection in Social Network

Kamal Sutaria¹, Dipesh Joshi², C K Bhensdadiya³, Kruti Khalpada⁴

^1,2,3,4C.U.SHAH University, Wadhwan, Gujarat

Abstract

Social network is one of the most important complex networks, which aims to describe the interactive relationship among a group of active actors that represent different kind of structure. Many systems in the real world such as human societies and different types of components can be modeled as social networks. We can represent such a network in terms of graphical community. Social Network Analysis provides inherent research due to success of social media sites and social content sharing facility. Social Network Analysis provides key terms to provide platform for industry to generate survey of product and facilitate to introduce new innovation ideas to public entity. Now a day, as increase the use of social media sites provide the entrepreneurs and user to define new concept of community creation that represents the relationship of users that might be interested in same kind of activity. To create such communities introduce new research area for researcher. This community detection is different from traditional clustering. In This paper, we propose new algorithm for community detection in social network to get some meaningful and important information.

Keywords

• Social Network Mining, Community Detection, Social Network Analysis, Data Mining

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Cite this Article as Follows

Kamal Sutaria, Dipesh Joshi, C K Bhensdadiya, Kruti Khalpada. An Adaptive Approximation Algorithm for Community Detection in Social Network. International Conference on Human Machine Interaction 2014. Vol. 1. Chennai: Association of Scientists, Developers and Faculties, 2014. 18-25. Print.