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.

翻译：二分图表示学习的一个基本挑战是如何提取信息丰富的节点嵌入。自监督学习（SSL）是应对这一挑战的一种有前景的范式。当前大多数二分图SSL方法基于对比学习，通过区分正负节点对来学习嵌入。对比学习通常需要大量负节点对，这可能导致计算负担和语义错误。本文提出一种新颖的协同表示学习模型（STERLING），无需负节点对即可学习节点嵌入。STERLING保留了二分图中独特的局部和全局协同关系：局部协同通过最大化跨类型和同类型正节点对的相似性来捕捉，全局协同通过最大化共聚类的互信息来捕捉。理论分析表明，STERLING可提升嵌入空间中不同节点类型间的连通性。在多个基准数据集和任务上的广泛实证评估证明了STERLING在提取节点嵌入方面的有效性。