Community Detection in Large Hypergraphs

TL;DR

This paper introduces a scalable, accurate framework for community detection in large hypergraphs, capable of identifying complex community structures in vast networks with higher-order interactions.

Contribution

The work presents a novel, flexible model that outperforms existing algorithms in accuracy and speed for community detection in large hypergraphs.

Findings

Achieves higher accuracy than state-of-the-art algorithms

Scales efficiently to hypergraphs with millions of nodes

Effectively captures both assortative and disassortative communities

Abstract

Hypergraphs, describing networks where interactions take place among any number of units, are a natural tool to model many real-world social and biological systems. In this work we propose a principled framework to model the organization of higher-order data. Our approach recovers community structure with accuracy exceeding that of currently available state-of-the-art algorithms, as tested in synthetic benchmarks with both hard and overlapping ground-truth partitions. Our model is flexible and allows capturing both assortative and disassortative community structures. Moreover, our method scales orders of magnitude faster than competing algorithms, making it suitable for the analysis of very large hypergraphs, containing millions of nodes and interactions among thousands of nodes. Our work constitutes a practical and general tool for hypergraph analysis, broadening our understanding of the organization of real-world higher-order systems.