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

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Publicado no:arXiv.org (Dec 9, 2024), p. n/a
Autor principal: Hajij, Mustafa
Outros Autores: Papillon, Mathilde, Frantzen, Florian, Agerberg, Jens, AlJabea, Ibrahem, Ballester, Rubén, Battiloro, Claudio, Bernárdez, Guillermo, Birdal, Tolga, Brent, Aiden, Chin, Peter, Escalera, Sergio, Fiorellino, Simone, Odin Hoff Gardaa, Gopalakrishnan, Gurusankar, Govil, Devendra, Hoppe, Josef, Maneel Reddy Karri, Khouja, Jude, Lecha, Manuel, Livesay, Neal, Meißner, Jan, Mukherjee, Soham, Nikitin, Alexander, Papamarkou, Theodore, Prílepok, Jaro, Karthikeyan Natesan Ramamurthy, Rosen, Paul, Guzmán-Sáenz, Aldo, Salatiello, Alessandro, Samaga, Shreyas N, Scardapane, Simone, Schaub, Michael T, Scofano, Luca, Spinelli, Indro, Telyatnikov, Lev, Truong, Quang, Walters, Robin, Yang, Maosheng, Zaghen, Olga, Zamzmi, Ghada, Zia, Ali, Miolane, Nina
Publicado em:
Cornell University Library, arXiv.org
Assuntos:
Algorithms
Python
Message passing
Computation
Source code
Packages
Neural networks
Machine learning
Combinatorial analysis
Domains
Topology
Vector spaces
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Resumo: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/.
ISSN:2331-8422
Fonte:Engineering Database