Community detection in large hypergraphs
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. Here, 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 14, 2023
- Source ID
- 10.1126/sciadv.adg9159
Entities
People
- Caterina De Bacco
- Federico Battiston
- Martina Contisciani
- Nicolò Ruggeri
Organizations
- Central European University
- ETH Zurich
- Max Planck Institute for Intelligent Systems