Kernel entropy graph isomorphism network for graph classification

Graph neural networks (GNNs) have been successfully applied to many graph classification tasks. However, most GNNs are based on message-passing neural network (MPNN) frameworks, making it difficult to utilize the structural information of the graph from multiple perspectives. To address the limitations of existing GNN methods, we incorporate structural information into graph embedding representation in two ways. On the one hand, the subgraph information in the neighborhood of a node is incorporated into the message passing process of GNN through graph entropy. On the other hand, we encode the path information in the graph with the help of an improved shortest path kernel. Then, these two parts of structural information are fused through the attention mechanism, which can capture the structural information of the graph and thus enrich the structural expression of graph neural network. Finally, the model is experimentally evaluated on seven publicly available graph classification datasets. Compared with the existing graph representation models, extensive experiments show that our model can better obtain graph representation and achieves more competitive performance.