  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.
