Bipartite Graph Convolutional Networks with Adversarial Domain Transfer

Bipartite graphs have been widely used in many applications such as recommender systems, search engines and so on. Recent works consider bipartite graphs as homogeneous graphs and apply graph convolution networks for link prediction or node classification. However, in bipartite graphs, there are two types of nodes which are from different domains such as users and items in recommender systems, and cannot be in the same embedding space. In this paper, we proposed a novel graph convolution operation to propagate in bipartite graph with less spatial and temporal complexities, and two mapping functions with adversarial constraints to transfer features between two domains. Experimental results show that the proposed model achieves the improved performance on the tasks of link prediction and recommendation in real-world scenarios.