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Computing exact P-values for community detection

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Abstract

Community detection is one of the most important issues in modern network science. Although numerous community detection algorithms have been proposed during the past decades, how to assess the statistical significance of one single community analytically and exactly still remains an open problem. In this paper, we present an analytical solution to calculate the exact p-value of a single community with the Erdös–Rényi model. Meanwhile, we propose a local search method for finding statistically significant communities based on the p-value minimization. Experimental results on both real networks and simulated networks demonstrate that our method is able to effectively detect true communities from different types of networks.

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Acknowledgements

This work was partially supported by the Natural Science Foundation of China under Grant Nos. 61972066 and 61572094.

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Authors and Affiliations

  1. School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China

    Zengyou He, Hao Liang, Zheng Chen & Yan Liu

  2. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Tuqiang Road, 116620, Dalian, China

    Zengyou He

  3. Institute of Information Engineering, CAS, Beijing, China

    Can Zhao

Authors
  1. Zengyou He
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  2. Hao Liang
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  3. Zheng Chen
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  4. Can Zhao
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  5. Yan Liu
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Correspondence to Zengyou He.

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Responsible editor: Evangelos Papalexakis.

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He, Z., Liang, H., Chen, Z. et al. Computing exact P-values for community detection. Data Min Knowl Disc 34, 833–869 (2020). https://doi.org/10.1007/s10618-020-00681-0

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