In contrastive learning, the choice of ``view'' controls the information that the representation captures and influences the performance of the model. However, leading graph contrastive learning methods generally produce views via random corruption or learning, which could lead to the loss of essential information and alteration of semantic information. An anchor view that maintains the essential information of input graphs for contrastive learning has been hardly investigated. In this paper, based on the theory of graph information bottleneck, we deduce the definition of this anchor view; put differently, \textit{the anchor view with essential information of input graph is supposed to have the minimal structural uncertainty}. Furthermore, guided by structural entropy, we implement the anchor view, termed \textbf{SEGA}, for graph contrastive learning. We extensively validate the proposed anchor view on various benchmarks regarding graph classification under unsupervised, semi-supervised, and transfer learning and achieve significant performance boosts compared to the state-of-the-art methods.

翻译：在对比学习中，“视图”的选择控制了表征捕获的信息，并影响模型的性能。然而，主流的图对比学习方法通常通过随机破坏或学习生成视图，这可能导致关键信息丢失和语义信息改变。能够保留输入图关键信息以供对比学习的锚定视图尚未得到充分研究。本文基于图信息瓶颈理论，推导出这一锚定视图的定义；换言之，**包含输入图关键信息的锚定视图应具有最小的结构不确定性**。此外，在结构熵的指导下，我们实现了锚定视图，称为**SEGA**，用于图对比学习。我们在无监督、半监督和迁移学习下的图分类等多个基准上广泛验证了所提出的锚定视图，并与最先进方法相比取得了显著的性能提升。