Improving Subgraph Representation Learning via Multi-View Augmentation

• URL: http://arxiv.org/abs/2205.13038v1
• Date: Wed, 25 May 2022 20:17:13 GMT
• Title: Improving Subgraph Representation Learning via Multi-View Augmentation
• Authors: Yili Shen, Jiaxu Yan, Cheng-Wei Ju, Jun Yi, Zhou Lin and Hui Guan
• Abstract summary: Subgraph representation learning based on Graph Neural Network (GNN) has broad applications in chemistry and biology. We develop a novel multiview augmentation mechanism to improve subgraph representation learning and thus the accuracy of downstream prediction tasks.
• Score: 6.907772294522709
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Subgraph representation learning based on Graph Neural Network (GNN) has broad applications in chemistry and biology, such as molecule property prediction and gene collaborative function prediction. On the other hand, graph augmentation techniques have shown promising results in improving graph-based and node-based classification tasks but are rarely explored in the GNN-based subgraph representation learning literature. In this work, we developed a novel multiview augmentation mechanism to improve subgraph representation learning and thus the accuracy of downstream prediction tasks. The augmentation technique creates multiple variants of subgraphs and embeds these variants into the original graph to achieve both high training efficiency, scalability, and improved accuracy. Experiments on several real-world subgraph benchmarks demonstrate the superiority of our proposed multi-view augmentation techniques.

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