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Normalized discrete Ricci flow used in community detection

Author

Listed:
  • Lai, Xin
  • Bai, Shuliang
  • Lin, Yong

Abstract

Complex network is a mainstream form of unstructured data in real world. Detecting communities in complex networks bears a wide range of applications. Different from the existing methods, which concentrate on applying statistics, graph theory or combinations, this work presents a new algorithm along a geometric avenue. By utilizing normalized discrete Ricci flow with modified σ-weight-sum, and employing a limit-free Ricci curvature using ∗-coupling, this algorithm prevents the graph from collapsing to a point, and eliminates a hyper parameter α in discrete Ollivier Ricci curvature. Besides, experiments on real-world networks and artificial networks have shown that this normalized algorithm has a matching or better result, and is more robust with regard to unnormalized one (Ni et al., 2019). The code is available at https://github.com/laiguzi/NormalizedRicciFlow.

Suggested Citation

  • Lai, Xin & Bai, Shuliang & Lin, Yong, 2022. "Normalized discrete Ricci flow used in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    • Handle: RePEc:eee:phsmap:v:597:y:2022:i:c:s0378437122002242
    DOI: 10.1016/j.physa.2022.127251
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    References listed on IDEAS

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    1. Chen, Xiangtao & Li, Juan, 2019. "Community detection in complex networks using edge-deleting with restrictions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 181-194.
    2. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    3. Li, Cong & Wang, Wenjing & Li, Jingya & Xu, Jiatuo & Li, Xiang, 2019. "Community detector on symptom networks with applications to fatty liver disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
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