Graph self-supervised learning (GSSL) has emerged as a compelling framework for extracting informative representations from graph-structured data without extensive reliance on labeled inputs. In this study, we introduce Graph Interplay (GIP), an innovative and versatile approach that significantly enhances the performance equipped with various existing GSSL methods. To this end, GIP advocates direct graph-level communications by introducing random inter-graph edges within standard batches. Against GIP's simplicity, we further theoretically show that \textsc{GIP} essentially performs a principled manifold separation via combining inter-graph message passing and GSSL, bringing about more structured embedding manifolds and thus benefits a series of downstream tasks. Our empirical study demonstrates that GIP surpasses the performance of prevailing GSSL methods across multiple benchmarks by significant margins, highlighting its potential as a breakthrough approach. Besides, GIP can be readily integrated into a series of GSSL methods and consistently offers additional performance gain. This advancement not only amplifies the capability of GSSL but also potentially sets the stage for a novel graph learning paradigm in a broader sense.

翻译：图自监督学习（GSSL）已成为一种引人注目的框架，能够在不过度依赖标注数据的情况下从图结构数据中提取信息丰富的表示。本研究提出了图交互（GIP），这是一种创新且通用的方法，能够显著提升多种现有GSSL方法的性能。为此，GIP通过在标准批次内引入随机图间边来促进直接的图级通信。尽管GIP设计简洁，我们进一步从理论上证明，GIP本质上通过结合图间消息传递与GSSL，实现了有理论依据的流形分离，从而形成更具结构化的嵌入流形，进而有益于一系列下游任务。我们的实证研究表明，GIP在多个基准测试中显著超越了主流GSSL方法的性能，凸显了其作为突破性方法的潜力。此外，GIP能够轻松集成到一系列GSSL方法中，并持续带来额外的性能提升。这一进展不仅增强了GSSL的能力，更可能在更广泛意义上为一种新颖的图学习范式奠定基础。