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Graph-based label propagation algorithm for community detection

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Abstract

Community detection is one of the most important topics in complex network analysis. Among a variety of approaches for detecting communities, the label propagation algorithm (LPA) is the simplest and time-efficient approach. However, the original label propagation algorithm is not stable due to the randomness in its propagation process. In this paper, we propose a graph-based label propagation algorithm (GLPA) to detect communities incorporating the node similarity and connectivity information during the propagation of the labels. First, we define node similarity between adjacent nodes, and change each node’s label to that of its most similar neighbor node. Based on the label propagation process, GLPA constructs a label propagation graph to get candidate communities. Then, GLPA calculates the connected components of the label propagation graph. Each connected component is treated as a candidate community in the next step. Second, GLPA constructs a weighted graph to obtain final communities, in which each connected component are treated as a super-node, and the number of edges lying between the corresponding components as the weight of edges. We compute the merging factor for each node in the weighted graph and merge super nodes with higher merging factor to its most similar node iteratively to reach the maximum complementary entropy. Compared with 8 other classical community detection algorithms on LFR artificial networks and 12 real world networks, the proposed algorithm GLPA shows preferable performance on stability, NMI, ARI, modularity.

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Acknowledgements

The authors of this paper are grateful to the National Natural Science Foundation of China (61673249, U1805263), the Natural Science Foundation of Shanxi (201801D121123)and the Research Project of Shanxi Scholarship Council of China (2017-014). The authors also gratefully acknowledge the anonymous referees for their constructive comments that improved this paper.

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Authors and Affiliations

  1. School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China

    Gui Yang, Wenping Zheng, Chenhao Che & Wenjian Wang

  2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Taiyuan, 030006, Shanxi, China

    Gui Yang, Wenping Zheng, Chenhao Che & Wenjian Wang

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  1. Gui Yang
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  2. Wenping Zheng
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  3. Chenhao Che
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  4. Wenjian Wang
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Correspondence to Wenjian Wang.

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Yang, G., Zheng, W., Che, C. et al. Graph-based label propagation algorithm for community detection. Int. J. Mach. Learn. & Cyber. 11, 1319–1329 (2020). https://doi.org/10.1007/s13042-019-01042-0

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