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Improved label propagation algorithm for overlapping community detection

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Abstract

Community detection plays an important role in the analysis of complex networks. However, overlapping community detection in real networks is still a challenge. To address the problems of pre-input parameters and label redundancy, an improved label propagation algorithm (ILPA) that adopts a method based on the influence factor is proposed in this paper. Theoretical analysis and experimental results on both synthetic and real datasets show that the ILPA detects that the overlapping community has higher accuracy compared to other existing methods.

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Acknowledgements

This paper is supported by Project supported by Key Scientific and Technological Research Projects in Henan Province (Grand No. 192102210125) and in part by the Study Abroad Activities of Science and Technology Project of Henan Province. In addition, the authors also will thank the anonymous reviewers for their comments and suggestions.

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  1. School of Computer Science and Technology, Zhoukou Normal University, Zhoukou, 466001, China

    Shi Dong

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  1. Shi Dong
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Correspondence to Shi Dong.

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Dong, S. Improved label propagation algorithm for overlapping community detection. Computing 102, 2185–2198 (2020). https://doi.org/10.1007/s00607-020-00836-3

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