Most graph contrastive learning (GCL) methods heavily rely on cross-view contrast, thus facing several concomitant challenges, such as the complexity of designing effective augmentations, the potential for information loss between views, and increased computational costs. To mitigate reliance on cross-view contrasts, we propose \ttt{SIGNA}, a novel single-view graph contrastive learning framework. Regarding the inconsistency between structural connection and semantic similarity of neighborhoods, we resort to soft neighborhood awareness for GCL. Specifically, we leverage dropout to obtain structurally-related yet randomly-noised embedding pairs for neighbors, which serve as potential positive samples. At each epoch, the role of partial neighbors is switched from positive to negative, leading to probabilistic neighborhood contrastive learning effect. Furthermore, we propose a normalized Jensen-Shannon divergence estimator for a better effect of contrastive learning. Surprisingly, experiments on diverse node-level tasks demonstrate that our simple single-view GCL framework consistently outperforms existing methods by margins of up to 21.74% (PPI). In particular, with soft neighborhood awareness, SIGNA can adopt MLPs instead of complicated GCNs as the encoder to generate representations in transductive learning tasks, thus speeding up its inference process by 109 times to 331 times. The source code is available at https://github.com/sunisfighting/SIGNA.

翻译：大多数图对比学习（GCL）方法严重依赖于跨视图对比，因此面临若干伴随性挑战，例如设计有效数据增强的复杂性、视图间潜在的信息损失以及计算成本的增加。为减轻对跨视图对比的依赖，我们提出 \ttt{SIGNA}，一种新颖的单视图图对比学习框架。针对邻域结构连接与语义相似性之间的不一致性，我们采用软邻域感知机制进行图对比学习。具体而言，我们利用丢弃法获取结构相关但随机噪声化的邻域嵌入对，作为潜在正样本。在每个训练周期，部分邻域的角色从正样本切换为负样本，从而产生概率性邻域对比学习效果。此外，我们提出归一化詹森-香农散度估计器以提升对比学习效果。令人惊讶的是，在多样化节点级任务上的实验表明，我们简洁的单视图GCL框架始终优于现有方法，最高可提升21.74%（PPI数据集）。特别地，通过软邻域感知机制，SIGNA能够在直推式学习任务中采用多层感知机而非复杂的图卷积网络作为编码器生成表征，从而将推理过程加速109至331倍。源代码发布于 https://github.com/sunisfighting/SIGNA。