Analysis of Dynamic Community Detection Algorithm

Ms. Prajakta Vispute, Prof. Dr. S. S. Sane


Graph mining and management is an important area of research in recent years. Different applications [4] result in graphs of different sizes and complexities. Correspondingly, the applications have different requirements for the underlying mining algorithms. Social network analysis [2] [3] [6] is one of the most dynamic area which is divided into four major themes graph theory, social networks, online social networks and graph mining. Community detection is a significant but challenging task in the field of social network analysis. Community can be considered as a summary of the whole network, which is easy to visualize and understand as well useful for further network analysis. Here, study of interaction network whose social network topology and exact time that nodes interact is considered. In an interaction network, an edge is associated with time stamp. The problem of detecting and analyzing communities in interaction network, where communities are dense and whose edges occur in shorttime intervals are analyzed. This work focuses on methods of identifying dynamic communities [9] in association with other parameters in interaction network which may analyse community effectively.

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