Higher-order Semantic-aware Adaptive Graph Contrastive Learning

Graph Contrastive Learning (GCL) has gained extensive attentions due to its success in label scarcity. GCL methods usually utilizes the graph neural network to learn node representation. However, the graph neural network can only aggregate direct neighbor features in each convolutional layer. The contextual dependencies and higher-order structural information among nodes can’t be captured by the local aggregation. In addition, existing GCL methods don’t consider semantic similarity when constructing positive and negative sample pairs of nodes. To address the problems, we propose a Higher-order Semantic-aware Adaptive Graph Contrastive Learning (HSAGCL) method. HSAGCL first extracts the semantic information of higher-order substructures of nodes, thereby integrating indirect neighbor features into node features. Then, HSAGCL adaptively generates augmented views of graphs through self-attention mechanism. The self-attention scores characterize the importance of correlation between nodes, providing a measure for the selection of positive and negative samples. Finally, HSAGCL learn effective graph-level discriminative representations for graph classification by jointly optimizing the contrastive loss and classification loss. Extensive experiments on 5 benchmark datasets show that the proposed HSAGCL achieves significant performance improvements in graph classification, with an average accuracy improvement of 8.94% over state-of-the-art methods.