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Overview of Various Dynamic Community Evolution Detection Approaches

Anu Jose, D. Chitraprasad, Simi P. Thomas

Abstract


Social network in a simplest form is a social structure consisting of units that are connected by various kinds of relations like friendship, common interest, financial exchange, dislike, knowledge or prestige. This can be represented by a combination of graph theory and data mining concepts. The easiest way to present social network in a mathematical way is graph representation where members are nodes of the graph and relations are edges between those nodes. One of the most interesting research topics is the dynamics of social groups which means analysis of group evolution over time. Having appropriate knowledge and methods for dynamic analysis, one may attempt to predict the future of the group, and then manage it properly in order to achieve or change this predicted future according to specific needs. Such ability would be a powerful tool in the hands of human resource managers, personnel recruitment, marketing, etc. In this paper we will provide the overview of some approaches which identify the evolution of dynamic communities

Keywords: social network, dynamic community detection, graph metrics, degree centrality, event detection.

 

Cite this Article:Anu Jose, D. Chitraprasad, Simi P Thomas, Overview of Various Dynamic Community Evolution Detection Approaches. Research & Reviews: A Journal of Embedded System & Applications. 3(1): 28–32p.


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