Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Ruben Ballester, Claudio Battiloro, Guillermo Bernárdez, Tolga Birdal, Aiden Brent, Peter Chin, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, Nina Miolane
{"title":"TopoX: A Suite of Python Packages for Machine Learning on Topological Domains","authors":"Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Ruben Ballester, Claudio Battiloro, Guillermo Bernárdez, Tolga Birdal, Aiden Brent, Peter Chin, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, Nina Miolane","doi":"arxiv-2402.02441","DOIUrl":null,"url":null,"abstract":"We introduce topox, a Python software suite that provides reliable and\nuser-friendly building blocks for computing and machine learning on topological\ndomains that extend graphs: hypergraphs, simplicial, cellular, path and\ncombinatorial complexes. topox consists of three packages: toponetx facilitates\nconstructing and computing on these domains, including working with nodes,\nedges and higher-order cells; topoembedx provides methods to embed topological\ndomains into vector spaces, akin to popular graph-based embedding algorithms\nsuch as node2vec; topomodelx is built on top of PyTorch and offers a\ncomprehensive toolbox of higher-order message passing functions for neural\nnetworks on topological domains. The extensively documented and unit-tested\nsource code of topox is available under MIT license at\nhttps://github.com/pyt-team.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"45 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-04","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-2402.02441","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
We introduce topox, a Python software suite that provides reliable and
user-friendly building blocks for computing and machine learning on topological
domains that extend graphs: hypergraphs, simplicial, cellular, path and
combinatorial complexes. topox consists of three packages: toponetx facilitates
constructing and computing on these domains, including working with nodes,
edges and higher-order cells; topoembedx provides methods to embed topological
domains into vector spaces, akin to popular graph-based embedding algorithms
such as node2vec; topomodelx is built on top of PyTorch and offers a
comprehensive toolbox of higher-order message passing functions for neural
networks on topological domains. The extensively documented and unit-tested
source code of topox is available under MIT license at
https://github.com/pyt-team.