Graph contrastive learning (GCL) has emerged as a representative paradigm in graph self-supervised learning, where negative samples are commonly regarded as the key to preventing model collapse and producing distinguishable representations. Recent studies have shown that GCL without negative samples can achieve state-of-the-art performance as well as scalability improvement, with bootstrapped graph latent (BGRL) as a prominent step forward. However, BGRL relies on a complex architecture to maintain the ability to scatter representations, and the underlying mechanisms enabling the success remain largely unexplored. In this paper, we introduce an instance-level decorrelation perspective to tackle the aforementioned issue and leverage it as a springboard to reveal the potential unnecessary model complexity within BGRL. Based on our findings, we present SGCL, a simple yet effective GCL framework that utilizes the outputs from two consecutive iterations as positive pairs, eliminating the negative samples. SGCL only requires a single graph augmentation and a single graph encoder without additional parameters. Extensive experiments conducted on various graph benchmarks demonstrate that SGCL can achieve competitive performance with fewer parameters, lower time and space costs, and significant convergence speedup.

翻译：图对比学习（GCL）已成为图自监督学习中的代表性范式，其中负样本通常被视为防止模型坍塌和产生可区分表示的关键。近期研究表明，无负样本的GCL能够取得与最先进方法相当的性能，并提升可扩展性，其中自举图潜变量（BGRL）是一个重要进展。然而，BGRL依赖复杂架构来维持表示散布能力，其成功背后的潜在机制尚未得到充分探索。本文引入实例级去相关视角来解决上述问题，并将其作为跳板揭示BGRL中可能存在的冗余模型复杂性。基于研究发现，我们提出了SGCL——一种简单而有效的GCL框架，该框架利用连续两次迭代的输出作为正样本对，从而消除了负样本。SGCL仅需单次图增广和单图编码器，无需额外参数。在多种图基准上的大量实验表明，SGCL能以更少的参数、更低的时间和空间成本实现具有竞争力的性能，并显著加速收敛。