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

Fuente: arXiv

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Autori principali:	Hajij, Mustafa, Papillon, Mathilde, Frantzen, Florian, Agerberg, Jens, AlJabea, Ibrahem, Ballester, Rubén, Battiloro, Claudio, Bernárdez, Guillermo, Birdal, Tolga, Brent, Aiden, Chin, Peter, Escalera, Sergio, Fiorellino, Simone, Gardaa, Odin Hoff, Gopalakrishnan, Gurusankar, Govil, Devendra, Hoppe, Josef, Karri, Maneel Reddy, Khouja, Jude, Lecha, Manuel, Livesay, Neal, Meißner, Jan, Mukherjee, Soham, Nikitin, Alexander, Papamarkou, Theodore, Prílepok, Jaro, Ramamurthy, Karthikeyan Natesan, 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
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence Mathematical Software Computation
Accesso online:	Acceder al recurso
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author	Hajij, Mustafa Papillon, Mathilde Frantzen, Florian Agerberg, Jens AlJabea, Ibrahem Ballester, Rubén Battiloro, Claudio Bernárdez, Guillermo Birdal, Tolga Brent, Aiden Chin, Peter Escalera, Sergio Fiorellino, Simone Gardaa, Odin Hoff Gopalakrishnan, Gurusankar Govil, Devendra Hoppe, Josef Karri, Maneel Reddy Khouja, Jude Lecha, Manuel Livesay, Neal Meißner, Jan Mukherjee, Soham Nikitin, Alexander Papamarkou, Theodore Prílepok, Jaro Ramamurthy, Karthikeyan Natesan 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
author_facet	Hajij, Mustafa Papillon, Mathilde Frantzen, Florian Agerberg, Jens AlJabea, Ibrahem Ballester, Rubén Battiloro, Claudio Bernárdez, Guillermo Birdal, Tolga Brent, Aiden Chin, Peter Escalera, Sergio Fiorellino, Simone Gardaa, Odin Hoff Gopalakrishnan, Gurusankar Govil, Devendra Hoppe, Josef Karri, Maneel Reddy Khouja, Jude Lecha, Manuel Livesay, Neal Meißner, Jan Mukherjee, Soham Nikitin, Alexander Papamarkou, Theodore Prílepok, Jaro Ramamurthy, Karthikeyan Natesan 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
contents	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/.
format	Preprint
id	arxiv_https___arxiv_org_abs_2402_02441
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	TopoX: A Suite of Python Packages for Machine Learning on Topological Domains Hajij, Mustafa Papillon, Mathilde Frantzen, Florian Agerberg, Jens AlJabea, Ibrahem Ballester, Rubén Battiloro, Claudio Bernárdez, Guillermo Birdal, Tolga Brent, Aiden Chin, Peter Escalera, Sergio Fiorellino, Simone Gardaa, Odin Hoff Gopalakrishnan, Gurusankar Govil, Devendra Hoppe, Josef Karri, Maneel Reddy Khouja, Jude Lecha, Manuel Livesay, Neal Meißner, Jan Mukherjee, Soham Nikitin, Alexander Papamarkou, Theodore Prílepok, Jaro Ramamurthy, Karthikeyan Natesan 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 Machine Learning Artificial Intelligence Mathematical Software Computation 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/.
title	TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
topic	Machine Learning Artificial Intelligence Mathematical Software Computation
url	https://arxiv.org/abs/2402.02441