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

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arxiv 2402.02441 v5 pith:IUH6ZMBS submitted 2024-02-04 cs.LG cs.AIcs.MSstat.CO

Mustafa Hajij , Mathilde Papillon , Florian Frantzen , Jens Agerberg , Ibrahem AlJabea , Rubén Ballester , Claudio Battiloro , Guillermo Bernárdez
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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 Mei{ss}ner 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
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classification cs.LGcs.AIcs.MSstat.CO
keywords domainstopologicaltopoxcomputinggithubhigher-orderhttpslearning
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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/}{https://pyt-team.github.io/.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics

    cs.LG 2024-11 conditional novelty 7.0 of 10

    HOPNet uses combinatorial complex topology plus a physics-inspired message schedule to learn rigid body collisions, reporting lower rollout error and better OOD generalization than graph-based baselines.

  2. Cosmology with Topological Deep Learning

    astro-ph.CO 2025-05 conditional novelty 6.0 of 10

    Topological neural networks using tetrahedra, clusters and hyperedges built from halo catalogs lower inference error on Omega_m by 22% and on sigma_8 by up to 60% versus graph neural networks on Quijote.

  3. How Does Topology Bias Distort Message Passing? A Dirichlet Energy Perspective

    cs.IR 2024-11 conditional novelty 5.0 of 10

    TSP, a test-time simplicial message-passing post-processor, reduces popularity bias and substantially improves tail-item recommendation in graph recommenders.

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