  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

Physics and Society;Social and Information Networks;Methodology