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Mathematical Problems in Engineering
Volume 2016, Article ID 9850927, 10 pages
http://dx.doi.org/10.1155/2016/9850927
Research Article

Semisupervised Community Detection by Voltage Drops

1School of Computer Science and Technology, Liaoning Normal University, Dalian, Liaoning 116081, China
2School of Urban and Environmental Science, Liaoning Normal University, Dalian, Liaoning 116029, China
3College of Business Administration, Dalian University of Finance and Economics, Dalian, Liaoning 116622, China

Received 3 November 2015; Revised 28 December 2015; Accepted 30 March 2016

Academic Editor: Pubudu N. Pathirana

Copyright © 2016 Min Ji 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

Many applications show that semisupervised community detection is one of the important topics and has attracted considerable attention in the study of complex network. In this paper, based on notion of voltage drops and discrete potential theory, a simple and fast semisupervised community detection algorithm is proposed. The label propagation through discrete potential transmission is accomplished by using voltage drops. The complexity of the proposal is for the sparse network with vertices and edges. The obtained voltage value of a vertex can be reflected clearly in the relationship between the vertex and community. The experimental results on four real networks and three benchmarks indicate that the proposed algorithm is effective and flexible. Furthermore, this algorithm is easily applied to graph-based machine learning methods.