基于对抗图卷积网络的链接预测模型

大部分的链接预测模型在挖掘节点相似性时过于依赖已知的链接信息,但在真实世界中,已知的观测链接数量通常较少.因此,为了提高模型的鲁棒性,需要提高解耦模型对链接信息的依赖并挖掘节点的潜在特征.文中考虑节点特征和链接之间的潜在关系,提出基于对抗图卷积网络的链接预测模型.首先利用节点间的相似性度量填充邻接矩阵中部分未知链接,缓解链接稀疏对图卷积模型的影响.再利用对抗网络深度挖掘节点特征和链接之间的潜在联系,降低模型对链接的依赖.在真实数据集上的实验表明,文中模型在链接预测问题上具有较好的表现力,在链接稀疏的情况下性能依旧较稳定,同时适用于大规模数据集.

Link Prediction Model Based on Adversarial Graph Convolutional Network

Most link prediction models rely too much on the known link information while mining node similarity. However, the number of the known observed links is small in the real world. To improve the robustness of the model, it is crucial to decouple the dependence of the model on the link information and mine the underlying features of nodes. In this paper, a link prediction model based on adversarial graph convolutional network is proposed with the consideration of the potential relationship between node features and links. Firstly, the similarity metric between nodes is utilized to fill in some unknown links in the adjacency matrix to alleviate the influence of link sparsity on the graph convolution model. Then, the adversarial network is employed to deeply mine the underlying connections between node features and links to reduce the dependence of the model on links. Experiments on real datasets show that the proposed model achieves better performance on link prediction problem and the performance remains relatively stable under link sparsity. Moreover, the proposed model is applicable to large-scale datasets.