TY  - STD
AU  - Mustafa Hajij
AU  - Mathilde Papillon
AU  - Florian Frantzen
AU  - Jens Agerberg
AU  - Ibrahem AlJabea
AU  - Ruben Ballester
AU  - Claudio Battiloro
AU  - Guillermo Bernardez
AU  - Tolga Birdal
AU  - Aiden Brent
AU  - Peter Chin
AU  - Sergio Escalera
AU  - Simone Fiorellino
AU  - Odin Hoff Gardaa
AU  - Gurusankar Gopalakrishnan
AU  - Devendra Govil
AU  - Josef Hoppe
AU  - Maneel Reddy Karri
AU  - Jude Khouja
AU  - Manuel Lecha
AU  - Neal Livesay
AU  - Jan Meibner
AU  - Soham Mukherjee
AU  - Alexander Nikitin
AU  - Theodore Papamarkou
AU  - Jaro Prilepok
AU  - Karthikeyan Natesan Ramamurthy
AU  - Paul Rosen
AU  - Aldo Guzman-Saenz
AU  - Alessandro Salatiello
AU  - Shreyas N. Samaga
AU  - Simone Scardapane
AU  - Michael T. Schaub
AU  - Luca Scofano
AU  - Indro Spinelli
AU  - Lev Telyatnikov
AU  - Quang Truong
AU  - Robin Walters
AU  - Maosheng Yang
AU  - Olga Zaghen
AU  - Ghada Zamzmi
AU  - Ali Zia
AU  - Nina Miolane
PY  - 2024//
TI  - TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
N2  - 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.
UR  - https://arxiv.org/abs/2402.02441
L1  - http://refbase.cvc.uab.es/files/HPF2024.pdf
N1  - HUPBA
ID  - Mustafa Hajij2024
ER  -