Abstract: Node classification is widely used in social network and other network data. In order to study node classification, generative adversarial network (GAN) is used to obtain node representation, so as to obtain a good node classification effect. On this basis, a node classification-generative adversa- rial network (NC-GAN) model is proposed. This model uses GAN to conduct a binary game, considers the connectivity distribution in the network and the similarity between nodes to obtain the node representation that better fits the network, and then classifies the node representation to obtain a good classification effect. In order to verify the effect, the proposal is compared with DeepWalk, GraphGAN and other node representation model and graph convolutional network model  in terms of link prediction and node classification. The model is only weaker than the GraphGAN model in link prediction, but it is better than other models in node classification.

Key words: generative adversarial network, multi-label, node classification