Brenda Praggastis, Sinan Aksoy, Dustin Arendt, Mark Bonicillo, Cliff Joslyn, Emilie Purvine, Madelyn Shapiro, Ji Young Yun
{"title":"HyperNetX: A Python package for modeling complex network data as hypergraphs","authors":"Brenda Praggastis, Sinan Aksoy, Dustin Arendt, Mark Bonicillo, Cliff Joslyn, Emilie Purvine, Madelyn Shapiro, Ji Young Yun","doi":"arxiv-2310.11626","DOIUrl":null,"url":null,"abstract":"HyperNetX (HNX) is an open source Python library for the analysis and\nvisualization of complex network data modeled as hypergraphs. Initially\nreleased in 2019, HNX facilitates exploratory data analysis of complex networks\nusing algebraic topology, combinatorics, and generalized hypergraph and graph\ntheoretical methods on structured data inputs. With its 2023 release, the\nlibrary supports attaching metadata, numerical and categorical, to nodes\n(vertices) and hyperedges, as well as to node-hyperedge pairings (incidences).\nHNX has a customizable Matplotlib-based visualization module as well as\nHypernetX-Widget, its JavaScript addon for interactive exploration and\nvisualization of hypergraphs within Jupyter Notebooks. Both packages are\navailable on GitHub and PyPI. With a growing community of users and\ncollaborators, HNX has become a preeminent tool for hypergraph analysis.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"15 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Mathematical Software","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2310.11626","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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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