Isomorphic Graph Classification Model Based on Reconstruction Error

Abstract

At present, the application of deep learning method in graph classification model focuses on the migration of convolutional neural network to graph data field, including redefinition of convolutional layer and pooling layer. Generalization of convolution operation to graph data is an effective method. However, both the convolutional layer and the global pooling layer have great room for improvement, especially in the extraction of network topology information. A new isomorphism classification model based on reconstruction error is proposed. On the one hand, WaveGIC is used to improve the ability of extracting topology information. On the other hand, multi-attention mechanism is used to represent the whole picture, which enables the model to pay attention to the information of key nodes. Due to the network deepening process, the characteristic expression of local topological structure is less and less obvious. Based on the classification loss, the reconstruction error loss is added to make the classifier consider the node characteristics and topology structure of the graph at the same time. Experimental results on the benchmark data set show that the proposed method has high accuracy of graph classification.

Keywords

• |graph neural network (gnn)|graph classification|reconstruction error|attention mechanism