SAIL: Self-Augmented Graph Contrastive Learning
This addresses the challenge of learning robust node representations in unsupervised graph scenarios, offering an incremental improvement over existing methods.
The paper tackles the problem of unstable performance in unsupervised graph neural networks by proposing SAIL, a self-augmented contrastive learning framework with self-distilling regularization, which achieves competitive or better results on benchmark datasets, even with a single GNN layer.
This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario. Specifically, we derive a theoretical analysis and provide an empirical demonstration about the non-steady performance of GNNs over different graph datasets, when the supervision signals are not appropriately defined. The performance of GNNs depends on both the node feature smoothness and the locality of graph structure. To smooth the discrepancy of node proximity measured by graph topology and node feature, we proposed SAIL - a novel \underline{S}elf-\underline{A}ugmented graph contrast\underline{i}ve \underline{L}earning framework, with two complementary self-distilling regularization modules, \emph{i.e.}, intra- and inter-graph knowledge distillation. We demonstrate the competitive performance of SAIL on a variety of graph applications. Even with a single GNN layer, SAIL has consistently competitive or even better performance on various benchmark datasets, comparing with state-of-the-art baselines.