  Bipartite graph embedding has recently attracted much attention due to the
fact that bipartite graphs are widely used in various application domains. Most
previous methods, which adopt random walk-based or reconstruction-based
objectives, are typically effective to learn local graph structures. However,
the global properties of bipartite graph, including community structures of
homogeneous nodes and long-range dependencies of heterogeneous nodes, are not
well preserved. In this paper, we propose a bipartite graph embedding called
BiGI to capture such global properties by introducing a novel local-global
infomax objective. Specifically, BiGI first generates a global representation
which is composed of two prototype representations. BiGI then encodes sampled
edges as local representations via the proposed subgraph-level attention
mechanism. Through maximizing the mutual information between local and global
representations, BiGI enables nodes in bipartite graph to be globally relevant.
Our model is evaluated on various benchmark datasets for the tasks of top-K
recommendation and link prediction. Extensive experiments demonstrate that BiGI
achieves consistent and significant improvements over state-of-the-art
baselines. Detailed analyses verify the high effectiveness of modeling the
global properties of bipartite graph.
