  Graph contrastive learning (GCL) has emerged as an effective tool for
learning unsupervised representations of graphs. The key idea is to maximize
the agreement between two augmented views of each graph via data augmentation.
Existing GCL models mainly focus on applying \textit{identical augmentation
strategies} for all graphs within a given scenario. However, real-world graphs
are often not monomorphic but abstractions of diverse natures. Even within the
same scenario (e.g., macromolecules and online communities), different graphs
might need diverse augmentations to perform effective GCL. Thus, blindly
augmenting all graphs without considering their individual characteristics may
undermine the performance of GCL arts.To deal with this, we propose the first
principled framework, termed as \textit{G}raph contrastive learning with
\textit{P}ersonalized \textit{A}ugmentation (GPA), to advance conventional GCL
by allowing each graph to choose its own suitable augmentation operations.In
essence, GPA infers tailored augmentation strategies for each graph based on
its topology and node attributes via a learnable augmentation selector, which
is a plug-and-play module and can be effectively trained with downstream GCL
models end-to-end. Extensive experiments across 11 benchmark graphs from
different types and domains demonstrate the superiority of GPA against
state-of-the-art competitors.Moreover, by visualizing the learned augmentation
distributions across different types of datasets, we show that GPA can
effectively identify the most suitable augmentations for each graph based on
its characteristics.
