基于生成对抗网络的多标签节点分类研究

Multi-label node classification based on generative adversarial network

节点分类被广泛应用于社交网络等网络数据处理之中,为了进行节点分类研究,考虑使用生成对抗网络GAN来得到节点表示,从而得到良好的节点分类效果。在此基础上,提出了节点分类生成对抗网络NC-GAN模型。该模型通过生成对抗网络进行二元博弈,同时考虑网络中的连通性分布和节点之间的相似度,以获得更加拟合网络的节点表示,再通过节点表示进行分类,获得良好的分类效果。为了验证效果,与DeepWalk、GraphGAN等节点表示模型和图卷积网络模型分别在链接预测和节点分类2方面进行对比,在链接预测上该模型仅弱于GraphGAN模型,但在节点分类上均优于其他模型。

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.