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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/.

Details

1009240
Subject
Algorithms;
Python;
Message passing;
Computation;
Source code;
Packages;
Neural networks;
Machine learning;
Combinatorial analysis;
Domains;
Topology;
Vector spaces
Identifier / keyword
Machine Learning; Artificial Intelligence; Mathematical Software; Computation
URL
http://arxiv.org/abs/2402.02441
Title
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
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; 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
Publication title
arXiv.org; Ithaca
Publication year
2024
Publication date
Dec 9, 2024
Section
Computer Science; Statistics
Publisher
Cornell University Library, arXiv.org
Source
arXiv.org
Place of publication
Ithaca
Country of publication
United States
University/institution
Cornell University Library arXiv.org
Publication subject
Biology, Statistics, Physics, Mathematics, Engineering--Electrical Engineering, Computers--Computer Engineering, Business And Economics--Banking And Finance
e-ISSN
2331-8422
Source type
Working Paper
Language of publication
English
Document type
Working Paper
Publication history
 
 
Online publication date
2024-12-10
Milestone dates
2024-02-04 (Submission v1); 2024-02-06 (Submission v2); 2024-02-07 (Submission v3); 2024-02-17 (Submission v4); 2024-12-09 (Submission v5)
Publication history
 
 
   First posting date
10 Dec 2024
ProQuest document ID
2923593582
Document URL
https://www.proquest.com/working-papers/topox-suite-python-packages-machine-learning-on/docview/2923593582/se-2?accountid=208611
Full text outside of ProQuest
http://arxiv.org/abs/2402.02441
Copyright
© 2024. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2024-12-11
Database
ProQuest One Academic