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

Deng, Y.; Zhang, R.; Xu, P.; Ma, J.; Gu, Q.

doi:10.1101/2023.10.01.560404

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bioRxiv · 10.1101/2023.10.01.560404

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

Deng, Y.·Zhang, R.·Xu, P.·Ma, J.·Gu, Q.

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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.

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Deng, Y., Zhang, R., Xu, P., Ma, J., Gu, Q.. 2023-10-02. PhyGCN: Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning. https://doi.org/10.1101/2023.10.01.560404

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