Community detection in sample networks generated from Gaussian mixture model

Authors:
Ling Zhao;Tingzhan Liu;Jian Liu
Affiliations:
Beijing University of Posts and Telecommunications, Beijing, P.R. China;School of Sciences, Communication University of China, Beijing, P.R. China;LMAM and School of Mathematical Sciences, Peking University, Beijing, P.R. China
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Year:
2011

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Abstract

Detecting communities in complex networks is of great importance in sociology, biology and computer science, disciplines where systems are often represented as networks. In this paper, we use the coarse-grained-diffusion-distance based agglomerative algorithm to uncover the community structure exhibited by sample networks generated from Gaussian mixture model, in which the connectivity of the network is induced by a metric. The present algorithm can identify the community structure in a high degree of efficiency and accuracy. An appropriate number of communities can be automatically determined without any prior knowledge about the community structure. The computational results on three artificial networks confirm the capability of the algorithm.