Disentangling Hyperedges Through the Lens of Category Theory
Abstract
Despite the promising results of disentangled representation learning in discovering latent patterns in graph-structured data, few studies have explored disentanglement for hypergraph-structured data. Integrating hyperedge disentanglement into hypergraph neural networks enables models to leverage hidden hyperedge semantics, such as unannotated relations between nodes, that are associated with labels. This paper presents an analysis of hyperedge disentanglement from a category-theoretical perspective and proposes a novel criterion for disentanglement derived from the naturality condition. Our proof-of-concept model experimentally showed the potential of the proposed criterion by successfully capturing functional relations of genes (nodes) in genetic pathways (hyperedges).
Cite
Text
Lee et al. "Disentangling Hyperedges Through the Lens of Category Theory." Advances in Neural Information Processing Systems, 2025.Markdown
[Lee et al. "Disentangling Hyperedges Through the Lens of Category Theory." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/lee2025neurips-disentangling/)BibTeX
@inproceedings{lee2025neurips-disentangling,
title = {{Disentangling Hyperedges Through the Lens of Category Theory}},
author = {Lee, Yoonho and Lee, Junseok and Seo, Sangwoo and Kim, Sungwon and Kim, Yeongmin and Park, Chanyoung},
booktitle = {Advances in Neural Information Processing Systems},
year = {2025},
url = {https://mlanthology.org/neurips/2025/lee2025neurips-disentangling/}
}