计算机科学与探索2019，Vol.13Issue(4)：554-562,9.
基于生成式对抗网络的链路预测方法*
Link Prediction Based on Generative Adversarial Networks*
摘要
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.关键词
链路预测/网络图编码/生成式对抗网络(GAN)Key words
link prediction/ graph labeling/ generative adversarial networks (GAN)分类
信息技术与安全科学引用本文复制引用
丁玥,黄玲,王昌栋..基于生成式对抗网络的链路预测方法*[J].计算机科学与探索,2019,13(4):554-562,9.基金项目
The Scientific Research Foundation of Shanghai Lixin University of Accounting and Finance under Grant No. 1419080006012 (上海立信会计金融学院科研启动基金项目). （上海立信会计金融学院科研启动基金项目）
