Abstract: With the widespread existence of graph data, the development of graph convolutional neural networks (GCNNs) is becoming faster and faster. According to the different definitions of the convolution operator, GCNNs can be roughly divided into two categories: one based on spectral methods and the other based on spatial methods. Firstly, representative models of these two categories and their connections are discussed in detail, and then the graph pooling operations are comprehensively summarized. Furthermore, the extensive applications of GCNNs in various fields are introduced, and several possible development directions of GCNNs are proposed. Finally, a conclusion is done.

Key words: graph data, convolution operator, graph convolutional neural network