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Community-Invariant Graph Contrastive Learning
May 3, 2024, 4:53 a.m. | Shiyin Tan, Dongyuan Li, Renhe Jiang, Ying Zhang, Manabu Okumura
cs.LG updates on arXiv.org arxiv.org
Abstract: Graph augmentation has received great attention in recent years for graph contrastive learning (GCL) to learn well-generalized node/graph representations. However, mainstream GCL methods often favor randomly disrupting graphs for augmentation, which shows limited generalization and inevitably leads to the corruption of high-level graph information, i.e., the graph community. Moreover, current knowledge-based graph augmentation methods can only focus on either topology or node features, causing the model to lack robustness against various types of noise. To …
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