ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2402.02441
40
12

TopoX: A Suite of Python Packages for Machine Learning on Topological Domains

4 February 2024
Mustafa Hajij
Mathilde Papillon
Florian Frantzen
Jens Agerberg
Ibrahem AlJabea
Rubén Ballester
Claudio Battiloro
Guillermo Bernárdez
Tolga Birdal
Aiden Brent
Peter Chin
Sergio Escalera
Simone Fiorellino
Odin Hoff Gardaa
Gurusankar Gopalakrishnan
D. Govil
Josef Hoppe
Maneel Reddy Karri
Jude Khouja
M. Lecha
Neal Livesay
Jan Meissner
Soham Mukherjee
Alexander Nikitin
Theodore Papamarkou
Jaro Prílepok
Karthikeyan N. 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
ArXivPDFHTML
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://pyt-team.github.io/.

View on arXiv
Comments on this paper