Graph representation learning (GRL) makes considerable progress recently,
which encodes graphs with topological structures into low-dimensional
embeddings. Meanwhile, the time-consuming and costly process of annotating
graph labels manually prompts the growth of self-supervised learning (SSL)
techniques. As a dominant approach of SSL, Contrastive learning (CL) learns
discriminative representations by differentiating between positive and negative
samples. However, when applied to graph data, it overemphasizes global patterns
while neglecting local structures. To tackle the above issue, we propose
\underline{Local}-aware \underline{G}raph \underline{C}ontrastive
\underline{L}earning (\textbf{\methnametrim}), a self-supervised learning
framework that supplementarily captures local graph information with
masking-based modeling compared with vanilla contrastive learning. Extensive
experiments validate the superiority of \methname against state-of-the-art
methods, demonstrating its promise as a comprehensive graph representation
learner.

局部感知图对比学习 (LACL) 是一种自监督学习框架，通过基于掩膜的建模补充性地捕捉局部图信息，相对于纯粹的对比学习，有效解决了在应用于图数据时过于强调全局模式而忽略局部结构的问题。实验结果验证了 LACL 方法优于当前最先进方法，展示了其作为一种全面的图表示学习器的潜力。