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

TL;DR

TopoX is a comprehensive Python suite enabling construction, embedding, and neural network modeling on complex topological domains like hypergraphs and simplicial complexes, extending graph-based machine learning capabilities.

Contribution

Introduces TopoX, a modular Python toolkit for machine learning on advanced topological structures beyond traditional graphs.

Findings

Provides reliable tools for topological data analysis.

Enables embedding of complex topological domains into vector spaces.

Supports higher-order neural network operations on topological data.

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://pyt-team.github.io/}{https://pyt-team.github.io/.