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Applied Computational Intelligence and Soft Computing
Volume 2016 (2016), Article ID 3217612, 11 pages
http://dx.doi.org/10.1155/2016/3217612
Research Article

Local Community Detection Algorithm Based on Minimal Cluster

School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221008, China

Received 14 June 2016; Accepted 11 October 2016

Academic Editor: Wu Deng

Copyright © 2016 Yong Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.