Normalized discrete Ricci flow used in community detection

Xin Lai, Shuliang Bai and Yong Lin

Physica A: Statistical Mechanics and its Applications, 2022, vol. 597, issue C

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.

Keywords: Community detection; Normalized discrete Ricci flow (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:597:y:2022:i:c:s0378437122002242

DOI: 10.1016/j.physa.2022.127251

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