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 a geometric avenue. By utilizing normalized discrete Ricci flow with modified (sigma)-weight-sum, and employing a limit-free Ricci curvature using (ast)-coupling, this algorithm prevents the graph from collapsing to a point, and eliminates a hyper parameter (alpha) 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 url{}.

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  1. Lai, Xin; Bai, Shuliang; Lin, Yong: Normalized discrete Ricci flow used in community detection (2022)