Hypergraphx-data: a repository for higher-order network data

TL;DR

Hypergraphx-data is a comprehensive repository of real-world hypergraph datasets across various domains, supporting higher-order network analysis with user-friendly access and reproducibility features.

Contribution

It introduces a new repository of diverse hypergraph datasets, addressing the limitations of existing pairwise-focused network data sources.

Findings

Includes datasets from social, biological, and financial domains.

Supports weighted, directed, temporal, and multiplex hypergraphs.

Provides open formats, metadata, and verification for reproducibility.

Abstract

The availability of network datasets advances research in network science, machine learning and related fields by enabling empirical analyses and their reproducibility, algorithm development, model validation and benchmarking. Existing repositories, such as SNAP and Netzschleuder, have made traditional network datasets widely accessible with metadata, metrics, and basic visualizations. However, they primarily focus on pairwise interactions, limiting data access to systems with many-body interactions. To address this gap, we created hypergraphx-data, a repository of real-world hypergraph datasets for higher-order network analysis, spanning different domains from social networks to biology and finance, and supporting configurations such as weighted, directed, temporal, and multiplex hypergraphs. Each dataset includes relational information and metadata, provided in an open JSON format and a binarized format for Hypergraphx. We provide a user-friendly interface to facilitate browsing, filtering, and accessing the datasets, while also ensuring integrity and reproducibility through hash-based verification and data versioning. The repository is available at https://hgx-team.github.io/hypergraphx-data