SPGCL: Subgraph Pattern-Aware Graph Contrastive Learning for High-Order Structural Representation

Graph contrastive learning has emerged as a promising self-supervised approach for node representation learning, reducing reliance on human annotations. However, its limitation for modeling high-order structures leads to: loss of critical edges and nodes, diminished discriminability for higher-order semantics, and indistinguishable negative samples. To address these limitations, we propose SPGCL, a Subgraph Pattern-Aware Graph Contrastive Learning for Structural Representation. SPGCL enables the model to capture high-order structures by leveraging subgraph patterns. Specifically, SPGCL introduces subgraph patterns to differentiate high-order structures and preserve edges crucial for distinguishing these structures. In addition, it internalizes the subgraph pattern features into SP edge features, SP node features, and SP adjacency matrices to provide a more comprehensive structural representation. Furthermore, it utilizes subgraph pattern similarity and distance similarity, and restructures graph contrastive loss to sharpen negative-sample discrimination. Experiments on six real-world datasets demonstrate that SPGCL significantly outperforms state-of-the-art baselines.