Hypergraphx: a library for higher-order network analysis

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.

Publication
Journal of Complex Networks, Volume 11, Issue 3, cnad019
Martina Contisciani
Martina Contisciani
PhD student

My research focuses on the analysis of network data using statistical tools. My background is in Theoretical and Applied Statistics and I am interested in discovering new techniques, approaches and perspectives used in the analysis of data. I have been working on a project focused on modeling covariate information in community detection algorithms and I am involved in investigating the conditional independence assumption, underlying the statistical inference on network data.

Caterina De Bacco
Caterina De Bacco
CyberValley Research Group Leader

My research focuses on understanding, optimizing and predicting relations between the microscopic and macroscopic properties of complex large-scale interacting systems.

Nicolò Ruggeri
Nicolò Ruggeri
PhD student

My research interests include, but are not limited to, Probabilistic Learning and Network Science, as well as connected fields. In particular, I aim at understanding how current probabilistic models can be improved upon, both on a representation and training level. I am also fascinated by how different ideas and concepts from within and outside ML interpolate in interesting and novel developments. Therefore, I strive to keep a broader view on theoretical and practical insights originating from different fields.

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