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

Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Ruben Ballester, Claudio Battiloro, Guillermo Bernárdez, 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 Meißner, 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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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://github.com/pyt-team.
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TopoX:拓扑域机器学习 Python 软件包套件
我们介绍的 topox 是一套 Python 软件,它为拓扑域的计算和机器学习提供了可靠、用户友好的构建模块,拓扑域包括:超图、单曲面、单元、路径和组合复合物。topox 由三个软件包组成:toponetx 方便在这些域上构建和计算,包括处理节点、边和高阶单元;topoembedx 提供将拓扑域嵌入向量空间的方法,类似于流行的基于图的嵌入算法,如 node2vec;topomodelx 基于 PyTorch 构建,为拓扑域上的神经网络提供了高阶消息传递函数的综合工具箱。topox 的源代码经过大量文档和单元测试,可在 MIT 许可下使用:https://github.com/pyt-team。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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