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

翻译：从社会系统到生物系统，许多真实世界系统都具有高阶非二元交互的特征。这类系统可通过超图进行便捷描述，其中超边编码了任意数量单元之间的相互作用。本文介绍一款开源Python库——hypergraphx（HGX），它提供了用于高阶网络分析的算法与函数的全面集合。这些功能包括：不同高阶表示形式之间的数据转换方法、局部与介观尺度上高阶组织的多种度量指标、用于稀疏高阶数据的统计滤波器、丰富的静态与动态生成模型，以及多种具有高阶交互的动力学过程实现。我们的计算框架具有通用性，能够分析包含加权、有向、带符号、时序及多重群组交互的超图。通过多种可视化工具，我们为高阶数据提供直观洞察。配套代码附带扩展的高阶数据存储库，并通过对社会网络高阶交互的系统分析，展示了HGX分析真实世界系统的能力。该库被设计为一个持续发展的社区项目，将在未来持续扩展其功能。本软件开源地址为：https://github.com/HGX-Team/hypergraphx