SCGRL: Graph representation learning based on edge structure contrastive self-supervised framework

In recent years, significant advancements have been made in contrastive self-supervised learning for graphs. However, most of the existing methods start from the feature level and ignore the structural information. In this work, we propose a graph representation learning based on edge structure contrastive self-supervised framework (SCGRL), which leverages a novel edge-structure-based "masked edges vs. complementary edges" instance pairs to fully utilize the topological information of the graph, and attempts to reconstruct the original graph using the visible graph structure. In the feature processing, normal coded features are constrained with the coded features without gradient updating to enhance the encoder’s prediction ability for the masked representation. In addition, boundary losses are designed to ensure that the model can accurately distinguish between different instance pairs. We conduct extensive experiments on various benchmark datasets to demonstrate that SCGRL outperforms the state-of-the-art in different downstream tasks, especially link prediction.