%0 Generic
%T TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
%A Mustafa Hajij
%A Mathilde Papillon
%A Florian Frantzen
%A Jens Agerberg
%A Ibrahem AlJabea
%A Ruben Ballester
%A Claudio Battiloro
%A Guillermo Bernardez
%A Tolga Birdal
%A Aiden Brent
%A Peter Chin
%A Sergio Escalera
%A Simone Fiorellino
%A Odin Hoff Gardaa
%A Gurusankar Gopalakrishnan
%A Devendra Govil
%A Josef Hoppe
%A Maneel Reddy Karri
%A Jude Khouja
%A Manuel Lecha
%A Neal Livesay
%A Jan Meibner
%A Soham Mukherjee
%A Alexander Nikitin
%A Theodore Papamarkou
%A Jaro Prilepok
%A Karthikeyan Natesan Ramamurthy
%A Paul Rosen
%A Aldo Guzman-Saenz
%A Alessandro Salatiello
%A Shreyas N. Samaga
%A Simone Scardapane
%A Michael T. Schaub
%A Luca Scofano
%A Indro Spinelli
%A Lev Telyatnikov
%A Quang Truong
%A Robin Walters
%A Maosheng Yang
%A Olga Zaghen
%A Ghada Zamzmi
%A Ali Zia
%A Nina Miolane
%D 2024
%F Mustafa Hajij2024
%O HUPBA
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=4021), last updated on Fri, 14 Jun 2024 10:54:18 +0200
%X 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 this https URL.
%9 miscellaneous
%U https://arxiv.org/abs/2402.02441
%U http://refbase.cvc.uab.es/files/HPF2024.pdf