Bipartite Graph Neural Networks for Efficient Node Representation Learning

Existing Graph Neural Networks (GNNs) mainly focus on general structures, while the specific architecture on bipartite graphs---a crucial practical data form that consists of two distinct domains of nodes---is seldom studied. In this paper, we propose Bipartite Graph Neural Network (BGNN), a novel model that is domain-consistent, unsupervised, and efficient. At its core, BGNN utilizes the proposed Inter-domain Message Passing (IDMP) for message aggregation and Intra-domain Alignment (IDA) towards information fusion over domains, both of which are trained without requiring any supervision. Moreover, we formulate a multi-layer BGNN in a cascaded manner to enable multi-hop relation modeling while enjoying promising efficiency in training. Extensive experiments on several datasets of varying scales verify the effectiveness of BGNN compared to other counterparts. Particularly for the experiment on a large-scale bipartite graph dataset, the scalability of our BGNN is validated in terms of fast training speed and low memory cost.