LocalGCL: Local-aware Contrastive Learning for Graphs
Haojun Jiang Jiawei Sun Jie Li Chentao Wu
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.