  Bipartite graphs have been used to represent data relationships in many
data-mining applications such as in E-commerce recommendation systems. Since
learning in graph space is more complicated than in Euclidian space, recent
studies have extensively utilized neural nets to effectively and efficiently
embed a graph's nodes into a multidimensional space. However, this embedding
method has not yet been applied to large-scale bipartite graphs. Existing
techniques either cannot be scaled to large-scale bipartite graphs that have
limited labels or cannot exploit the unique structure of bipartite graphs,
which have distinct node features in two domains. Thus, we propose Cascade
Bipartite Graph Neural Networks, Cascade-BGNN, a novel node representation
learning for bipartite graphs that is domain-consistent, self-supervised, and
efficient. To efficiently aggregate information both across and within the two
partitions of a bipartite graph, BGNN utilizes a customized Inter-domain
Message Passing (IDMP) and Intra-domain Alignment (IDA), which is our
adaptation of adversarial learning, for message aggregation across and within
partitions, respectively. BGNN is trained in a self-supervised manner.
Moreover, we formulate a multi-layer BGNN in a cascaded training manner to
enable multi-hop relationship modeling while improving training efficiency.
Extensive experiments on several datasets of varying scales verify the
effectiveness and efficiency of BGNN over baselines. Our design is further
affirmed through theoretical analysis for domain alignment. The scalability of
BGNN is additionally verified through its demonstrated rapid training speed and
low memory cost over a large-scale real-world bipartite graph.
