Hypergraphx: a library for higher-order network analysis

• Published in: Journal of complex networks 2023-04, Vol.11 (3)
• Main Authors: Lotito, Quintino Francesco, Contisciani, Martina, De Bacco, Caterina, Di Gaetano, Leonardo, Gallo, Luca, Montresor, Alberto, Musciotto, Federico, Ruggeri, Nicolò, Battiston, Federico
• Format: Article
• Language: English
• ISSN: 2051-1329; 2051-1329
• DOI: 10.1093/comnet/cnad019

Abstract From social to biological systems, many real-world systems are characterized by higher-order, non-dyadic interactions. Such systems are conveniently described by hypergraphs, where hyperedges encode interactions among an arbitrary number of units. Here, we present an open-source python library, hypergraphx (HGX), providing a comprehensive collection of algorithms and functions for the analysis of higher-order networks. These include different ways to convert data across distinct higher-order representations, a large variety of measures of higher-order organization at the local and the mesoscale, statistical filters to sparsify higher-order data, a wide array of static and dynamic generative models, and an implementation of different dynamical processes with higher-order interactions. Our computational framework is general, and allows to analyse hypergraphs with weighted, directed, signed, temporal and multiplex group interactions. We provide visual insights on higher-order data through a variety of different visualization tools. We accompany our code with an extended higher-order data repository and demonstrate the ability of HGX to analyse real-world systems through a systematic analysis of a social network with higher-order interactions. The library is conceived as an evolving, community-based effort, which will further extend its functionalities over the years. Our software is available at https://github.com/HGX-Team/hypergraphx.