  Various graph contrastive learning models have been proposed to improve the
performance of learning tasks on graph datasets in recent years. While
effective and prevalent, these models are usually carefully customized. In
particular, although all recent researches create two contrastive views, they
differ greatly in view augmentations, architectures, and objectives. It remains
an open question how to build your graph contrastive learning model from
scratch for particular graph learning tasks and datasets. In this work, we aim
to fill this gap by studying how graph information is transformed and
transferred during the contrastive learning process and proposing an
information-aware graph contrastive learning framework called InfoGCL. The key
point of this framework is to follow the Information Bottleneck principle to
reduce the mutual information between contrastive parts while keeping
task-relevant information intact at both the levels of the individual module
and the entire framework so that the information loss during graph
representation learning can be minimized. We show for the first time that all
recent graph contrastive learning methods can be unified by our framework. We
empirically validate our theoretical analysis on both node and graph
classification benchmark datasets, and demonstrate that our algorithm
significantly outperforms the state-of-the-arts.
