Maximum-Entropy Random Walks on Hypergraphs

TL;DR

This paper introduces a maximum-entropy random walk model for directed hypergraphs, capturing higher-order interactions with mechanisms like broadcasting and merging, and employs entropy-based inference for analyzing complex systems.

Contribution

It develops a novel maximum-entropy framework for random walks on directed hypergraphs, incorporating broadcasting and merging interactions with entropy-based transition inference.

Findings

Framework effectively models higher-order interactions.

Iterative algorithms compute transition kernels.

Demonstrated on synthetic and real-world data.

Abstract

Random walks are fundamental tools for analyzing complex networked systems, including social networks, biological systems, and communication infrastructures. While classical random walks focus on pairwise interactions, many real-world systems exhibit higher-order interactions naturally modeled by hypergraphs. Existing random walk models on hypergraphs often focus on undirected structures or do not incorporate entropy-based inference, limiting their ability to capture directional flows, uncertainty, or information diffusion in complex systems. In this article, we develop a maximum-entropy random walk framework on directed hypergraphs with two interaction mechanisms: broadcasting where a pivot node activates multiple receiver nodes and merging where multiple pivot nodes jointly influence a receiver node. We infer a transition kernel via a Kullback--Leibler divergence projection onto constraints enforcing stochasticity and stationarity. The resulting optimality conditions yield a multiplicative scaling form, implemented using Sinkhorn--Schr\"odinger-type iterations with tensor contractions. We further analyze ergodicity, including projected linear kernels for broadcasting and tensor spectral criteria for polynomial dynamics in merging. The effectiveness of our framework is demonstrated with both synthetic and real-world examples.