NormalizedRicciFlow

This is an implementation of the paper Normalized Discrete Ricci Flow Used in Community Detection

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 $\sigma$-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.

cd NormalizedRicciFlow/StarRicciFlow/
./doit.sh