Hypergraphx: a library for higher-order network analysis

作     者：Lotito, Quintino Francesco Contisciani, Martina De Bacco, Caterina Di Gaetano, Leonardo Gallo, Luca Montresor, Alberto Musciotto, Federico Ruggeri, Nicolò Battiston, Federico 

作者机构：Department of Information Engineering and Computer Science University of Trento via Sommarive 9 Trento38123 Italy Max Planck Institute for Intelligent Systems Cyber Valley Tübingen72076 Germany Department of Network and Data Science Central European University Vienna1100 Austria Dipartimento di Fisica e Chimica Emilio Segrè Università di Palermo Viale delle Scienze Ed. 18 PalermoI-90128 Italy Department of Computer Science ETH Zürich8004 Switzerland 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Graph theory 

摘      要：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://***/HGX-Team/hypergraphx. Copyright © 2023, The Authors. All rights reserved.