Improving Subgraph Representation Learning via Multi-View Augmentation

Yili Shen, Cheng-Wei Ju, Jiaxu Yan, Jun Yi, Zhou Lin, Hui Guan

Keywords: Graph Learning, Subgraph Representation Learning, Graph Data Augmentation, Multi-view Augmentation

TL;DR: We develop a novel multi-view augmentation mechanism to improve subgraph representation learning models and thus the accuracy of downstream prediction tasks.

Abstract: Subgraph representation learning based on Graph Neural Network (GNN) has exhibited broad applications in scientific advancements, such as predictions of molecular structure-property relationships and collective cellular function. In particular, graph augmentation techniques have shown promising results in improving graph-based and node-based classification tasks. Still, they have rarely been explored in the existing GNN-based subgraph representation learning studies. In this study, we develop a novel multi-view augmentation mechanism to improve subgraph representation learning models and thus the accuracy of downstream prediction tasks. Our augmentation technique creates multiple variants of subgraphs and embeds these variants into the original graph to achieve highly improved training efficiency, scalability, and accuracy. Benchmark experiments on several real-world biological and physiological datasets demonstrate the superiority of our proposed multi-view augmentation techniques in subgraph representation learning.

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Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning