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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 109671, 8 pages
Research Article

Effective Semisupervised Community Detection Using Negative Information

1School of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
2School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China

Received 5 June 2014; Accepted 13 October 2014

Academic Editor: Qinggang Meng

Copyright © 2015 Dong Liu 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.


The semisupervised community detection method, which can utilize prior information to guide the discovery process of community structure, has aroused considerable research interests in the past few years. Most of the former works assume that the exact labels of some nodes are known in advance and presented in the forms of individual labels and pairwise constraints. In this paper, we propose a novel type of prior information called negative information, which indicates whether a node does not belong to a specific community. Then the semisupervised community detection algorithm is presented based on negative information to efficiently make use of this type of information to assist the process of community detection. The proposed algorithm is evaluated on several artificial and real-world networks and shows high effectiveness in recovering communities.