Community detection by label propagation with compression of flow

Jihui Han (), Wei Li, Zhu Su, Longfeng Zhao and Weibing Deng
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Jihui Han: Complexity Science Center, Institute of Particle Physics, Central China Normal University
Wei Li: Complexity Science Center, Institute of Particle Physics, Central China Normal University
Zhu Su: Complexity Science Center, Institute of Particle Physics, Central China Normal University
Longfeng Zhao: Complexity Science Center, Institute of Particle Physics, Central China Normal University
Weibing Deng: Complexity Science Center, Institute of Particle Physics, Central China Normal University

The European Physical Journal B: Condensed Matter and Complex Systems, 2016, vol. 89, issue 12, 1-11

Abstract: Abstract The label propagation algorithm (LPA) has been proved to be a fast and effective method for detecting communities in large complex networks. However, its performance is subject to the non-stable and trivial solutions of the problem. In this paper, we propose a modified label propagation algorithm LPAf to efficiently detect community structures in networks. Instead of the majority voting rule of the basic LPA, LPAf updates the label of a node by considering the compression of a description of random walks on a network. A multi-step greedy agglomerative strategy is employed to enable LPAf to escape the local optimum. Furthermore, an incomplete update condition is also adopted to speed up the convergence. Experimental results on both synthetic and real-world networks confirm the effectiveness of our algorithm.

Keywords: Statistical; and; Nonlinear; Physics (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1140/epjb/e2016-70264-6

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