Modularity Based Community Detection in Hypergraphs

TL;DR

This paper introduces h-Louvain, a scalable community detection algorithm for hypergraphs that optimizes a hypergraph modularity function, improving community detection accuracy through a Bayesian-optimized parameter tuning process.

Contribution

It adapts the Louvain algorithm for hypergraphs by dynamically tuning parameters to optimize hypergraph modularity, addressing limitations of direct application.

Findings

Improved community detection results on synthetic networks.

Enhanced performance on real-world hypergraph datasets.

Parameter tuning via Bayesian optimization is effective.

Abstract

In this paper, we propose a scalable community detection algorithm using hypergraph modularity function, h-Louvain. It is an adaptation of the classical Louvain algorithm in the context of hypergraphs. We observe that a direct application of the Louvain algorithm to optimize the hypergraph modularity function often fails to find meaningful communities. We propose a solution to this issue by adjusting the initial stage of the algorithm via carefully and dynamically tuned linear combination of the graph modularity function of the corresponding two-section graph and the desired hypergraph modularity function. The process is guided by Bayesian optimization of the hyper-parameters of the proposed procedure. Various experiments on synthetic as well as real-world networks are performed showing that this process yields improved results in various regimes.