An Efficient Entropy Flow on Weighted Graphs: Theory and Applications

TL;DR

This paper introduces a new entropy flow on weighted graphs that models probability evolution, offering a computationally efficient alternative to Ricci flow for large-scale graph analysis.

Contribution

It formulates a novel entropy flow framework, proves its theoretical properties, and demonstrates its effectiveness in community detection with significantly reduced computation time.

Findings

Achieves community detection accuracy comparable to Ricci flow.

Requires only 1.61%–3.20% of Ricci flow computation time.

Provides a rigorous theoretical foundation for entropy flow on graphs.

Abstract

We propose a novel entropy flow on weighted graphs, which provides a principled framework that characterizes the evolution of probability distributions over graph structures while sharing geometric intuition with discrete Ricci flow. We provide its rigorous formulation, establish its fundamental theoretical properties, and prove the long-time existence and convergence of its solutions. To demonstrate its applicability, we employ entropy flow for community detection in real-world networks. Empirically, it achieves detection accuracy fully comparable to that of discrete Ricci flow. Crucially, by avoiding computations of optimal transport distances and shortest paths, our approach overcomes the fundamental computational bottleneck of Ollivier and Lin-Lu-Yau Ricci flows. As a result, entropy flow requires only $1.61\%$-$3.20\%$ of the computation time of Ricci flow. These results indicate that entropy flow provides a theoretically rigorous and computationally efficient framework for large-scale graph analysis.