Normalized discrete Ricci flow used in community detection

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...

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Bibliographic Details
Published in:Physica A 2022-07, Vol.597, p.127251, Article 127251
Main Authors: Lai, Xin, Bai, Shuliang, Lin, Yong
Format: Article
Language:English
Subjects:
Community detection
Normalized discrete Ricci flow
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Summary: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.
ISSN:0378-4371
1873-2119
DOI:10.1016/j.physa.2022.127251