  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.
