Graph Contrastive Learning (GCL) establishes a new paradigm for learning
graph representations without human annotations. Although remarkable progress
has been witnessed recently, the success behind GCL is still left somewhat
mysterious. In this work, we first identify several critical design
considerations within a general GCL paradigm, including augmentation functions,
contrasting modes, contrastive objectives, and negative mining techniques.
Then, to understand the interplay of different GCL components, we conduct
extensive, controlled experiments over a set of benchmark tasks on datasets
across various domains. Our empirical studies suggest a set of general receipts
for effective GCL, e.g., simple topology augmentations that produce sparse
graph views bring promising performance improvements; contrasting modes should
be aligned with the granularities of end tasks. In addition, to foster future
research and ease the implementation of GCL algorithms, we develop an
easy-to-use library PyGCL, featuring modularized CL components, standardized
evaluation, and experiment management. We envision this work to provide useful
empirical evidence of effective GCL algorithms and offer several insights for
future research.

本文提出了一种利用 Graph Contrastive Learning 无人工标注学习图形表示的新范式，并通过对一系列基准任务和各种领域的数据集进行广泛、可控的实验，研究不同的 GCL 组件之间的相互作用，得出了一套有效 GCL 的一般特性与实现技巧。