  A fundamental challenge of bipartite graph representation learning is how to
extract informative node embeddings. Self-Supervised Learning (SSL) is a
promising paradigm to address this challenge. Most recent bipartite graph SSL
methods are based on contrastive learning which learns embeddings by
discriminating positive and negative node pairs. Contrastive learning usually
requires a large number of negative node pairs, which could lead to
computational burden and semantic errors. In this paper, we introduce a novel
synergistic representation learning model (STERLING) to learn node embeddings
without negative node pairs. STERLING preserves the unique local and global
synergies in bipartite graphs. The local synergies are captured by maximizing
the similarity of the inter-type and intra-type positive node pairs, and the
global synergies are captured by maximizing the mutual information of
co-clusters. Theoretical analysis demonstrates that STERLING could improve the
connectivity between different node types in the embedding space. Extensive
empirical evaluation on various benchmark datasets and tasks demonstrates the
effectiveness of STERLING for extracting node embeddings.
