Enhancing Graph Neural Networks for Graph Classification via Structure-Guided Adaptor

Graph classification is a fundamental task in graph machine learning, aiming to categorize entire graphs based on their structural information and node attributes. The graph classification task wildly existed in collaboration technology, like knowledge graph, social network analysis. However, the structure of graph samples often varies greatly within the graph dataset. This inherent diversity poses a challenge for existing Graph Neural Networks (GNNs) to make graph classification, as they often uniformly utilize the same model for all graph samples. In this paper, we propose a novel framework, GraphSGA, which enhances Graph Neural Networks for graph classification via tailoring models for each graph based on their strucure characteristics. Our framework consists of a graph topology encoder and a structure-guided adaptor. The encoder converts the graph topology into a vector, offering a rich structural representation that guides the adaptor to accordingly refine graph embeddings. This enables a structure-aware adaption to model when classify graphs with distinct structures, improving the overall classification accuracy. Comprehensive experimental results show that our method outperforms baseline methods with a significant improvement and is superior to state-of-the-art methods. Also, the proposed approach possesses good compatibility with various GNN backbones like GCN, GAT, GIN.