Hajij, M., Papillon, M., Frantzen, F., Agerberg, J., AlJabea, I., Ballester, R., ... & Miolane, N.
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 this https URL.
Citation
Hajij, M., Papillon, M., Frantzen, F., Agerberg, J., AlJabea, I., Ballester, R., ... & Miolane, N. (2024). TopoX: a suite of Python packages for machine learning on topological domains. Journal of Machine Learning Research, 25(374), 1-8.
BibTeX
@article{hajij2024topox,
title={TopoX: a suite of Python packages for machine learning on topological domains},
author={Hajij and Papillon and Frantzen and Agerberg and AlJabea and Ballester and Battiloro and Bern{\'a}rdez and Birdal and Brent and others (including Miolane)},
journal={Journal of Machine Learning Research},
volume={25},
number={374},
pages={1--8},
year={2024}
}
