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Mathematical Problems in Engineering
Volume 2014, Article ID 406485, 10 pages
Research Article

An Improved Local Community Detection Algorithm Using Selection Probability

School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China

Received 27 September 2013; Revised 18 December 2013; Accepted 19 December 2013; Published 12 January 2014

Academic Editor: Yuri Vladimirovich Mikhlin

Copyright © 2014 Shixiong Xia 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.


In order to find the structure of local community more effectively, we propose an improved local community detection algorithm ILCDSP, which improves the node selection strategy, and sets selection probability value for every candidate node. ILCDSP assigns nodes with different selection probability values, which are equal to the degree of the nodes to be chosen. By this kind of strategy, the proposed algorithm can detect the local communities effectively, since it can ensure the best search direction and avoid the local optimal solution. Various experimental results on both synthetic and real networks demonstrate that the quality of the local communities detected by our algorithm is significantly superior to the state-of-the-art methods.