Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning

Yihe Deng, Ruochi Zhang, Pan Xu, Jian Ma, Quanquan Gu

Abstract: Hypergraphs are powerful tools for modeling complex interactions across various domains, including biomedicine. However, learning meaningful node representations from hypergraphs remains a challenge. Existing supervised methods often lack generalizability, thereby limiting their real-world applications. We propose a new method, Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning (PhyGCN), which leverages hypergraph structure for self-supervision to enhance node representations. PhyGCN introduces a unique training strategy that integrates variable hyperedge sizes with self-supervised learning, enabling improved generalization to unseen data. Applications on multi-way chromatin interactions and polypharmacy side-effects demonstrate the effectiveness of PhyGCN. As a generic framework for high-order interaction datasets with abundant unlabeled data, PhyGCN holds strong potential for enhancing hypergraph node representations across various domains.

Submission Length: Regular submission (no more than 12 pages of main content)

Code: https://github.com/uclaml/PhyGCN

Assigned Action Editor: ~Ying_Wei1

Submission Number: 2564