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HyperNetX: A Python package for modeling complex network data as hypergraphs

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arxiv 2310.11626 v1 pith:BRHOJ5E7 submitted 2023-10-17 cs.MS

Brenda Praggastis , Sinan Aksoy , Dustin Arendt , Mark Bonicillo , Cliff Joslyn , Emilie Purvine , Madelyn Shapiro , Ji Young Yun This is my paper · ORCID
classification cs.MS
keywords dataanalysiscomplexhypergraphsvisualizationhypergraphhypernetxlibrary
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HyperNetX (HNX) is an open source Python library for the analysis and visualization of complex network data modeled as hypergraphs. Initially released in 2019, HNX facilitates exploratory data analysis of complex networks using algebraic topology, combinatorics, and generalized hypergraph and graph theoretical methods on structured data inputs. With its 2023 release, the library supports attaching metadata, numerical and categorical, to nodes (vertices) and hyperedges, as well as to node-hyperedge pairings (incidences). HNX has a customizable Matplotlib-based visualization module as well as HypernetX-Widget, its JavaScript addon for interactive exploration and visualization of hypergraphs within Jupyter Notebooks. Both packages are available on GitHub and PyPI. With a growing community of users and collaborators, HNX has become a preeminent tool for hypergraph analysis.

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  1. Enhancing the Utility of Higher-Order Information in Relational Learning

    cs.LG 2025-02 conditional novelty 6.0 of 10

    Graph-level GNNs with new hypergraph-based encodings beat hypergraph-specific GNNs on several benchmarks, and the encodings provably increase expressivity beyond graph-level encodings.

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