Graph contrastive learning (GCL), as a self-supervised learning method, can
solve the problem of annotated data scarcity. It mines explicit features in
unannotated graphs to generate favorable graph representations for downstream
tasks. Most existing GCL methods focus on the design of graph augmentation
strategies and mutual information estimation operations. Graph augmentation
produces augmented views by graph perturbations. These views preserve a locally
similar structure and exploit explicit features. However, these methods have
not considered the interaction existing in subgraphs. To explore the impact of
substructure interactions on graph representations, we propose a novel
framework called subgraph network-based contrastive learning (SGNCL). SGNCL
applies a subgraph network generation strategy to produce augmented views. This
strategy converts the original graph into an Edge-to-Node mapping network with
both topological and attribute features. The single-shot augmented view is a
first-order subgraph network that mines the interaction between nodes,
node-edge, and edges. In addition, we also investigate the impact of the
second-order subgraph augmentation on mining graph structure interactions, and
further, propose a contrastive objective that fuses the first-order and
second-order subgraph information. We compare SGNCL with classical and
state-of-the-art graph contrastive learning methods on multiple benchmark
datasets of different domains. Extensive experiments show that SGNCL achieves
competitive or better performance (top three) on all datasets in unsupervised
learning settings. Furthermore, SGNCL achieves the best average gain of 6.9\%
in transfer learning compared to the best method. Finally, experiments also
demonstrate that mining substructure interactions have positive implications
for graph contrastive learning.

本文提出了一种名为基于子图网络的对比学习的新框架，用于挖掘子结构与图表征之间的相互作用，可提高图像的非监督和迁移学习性能。