A Spatial Graph Convolutional Network based on Node Semantic for Graph Classification

抄録

Graph classification has gained significant attention in recent years due to its wide applications in many domains such as cheminformatics, bioinformatics and social networks. Graph neural networks have been proved to be an effective solution for graph classification because of their powerful ability of learning graph node features. However, existing spatial graph convolutional neural networks for node-labeled graph classification utilize one-hot encoding or graph kernel methods to initialize node features, leading to their inability to capture semantic dependencies among graph nodes, with the result of a decrease in graph classification accuracy. In this paper, we propose a Node Semantic-based Spatial Graph ConvolutionalNetwork (NSSGCN) for graph classification which integrates multi-scale node semantic into graph neural network with word embedding. Specifically, we construct multiple corpora of different granularity for a graph dataset, and then leverage the PV-DBOW model to extract multiscale node semantic information from built corpora. Then, we normalize non-Euclidean graph data into 3D tensor data by node ordering and receptive field constructing, during which we propose a node importance measurement considering both node semantic and topology. After that, we design a channel attention based spatial graph convolutional neural network to effectively learn graph feature vectors from these 3D tensor data. Finally, we apply a Dense layer followed by a softmax layer to the learned graph feature vectors to classify graphs. Experimental results show that our proposed method achieves superior graph classification accuracy compared with classical graph kernel methods and state-of-the-art spatial graph neural networks on six benchmark graph datasets. On average, our method achieves a remarkable accuracy improvement of 4.12% in graph classification.