TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
This is an incremental contribution that provides a software toolkit for researchers and practitioners working with topological data in machine learning.
The authors introduced TopoX, a Python software suite for machine learning on topological domains like hypergraphs and simplicial complexes, providing tools for construction, embedding, and neural network functions, with the code being extensively documented and available under an MIT license.
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/.