关键词: 链路预测, 网络图编码, 生成式对抗网络（GAN）

Abstract: In recent years, the problem of link prediction in the network has gradually arisen. Compared to traditional heuristic models, link prediction methods based on neural networks have gained the favor of researchers because of the advantage of self-learning. This paper proposes a novel link prediction method based on generative adversarial networks, termed WL-GAN (Weisfeiler-Lehman generative adversarial networks). It first uses a subgraph extraction algorithm and a subgraph encoding algorithm to construct a node pair subgraph, in which the node pair is the structural center. Then it obtains the corresponding adjacency matrix for each node pair of known relationships in the network, which is used to train the generative adversarial networks. A discriminator which can determine whether there is a link in the node pair can be obtained. The experimental results show that WL-GAN achieves excellent performance and stability.

Key words: link prediction, graph labeling, generative adversarial networks (GAN)