Recently, contrastive learning (CL) has emerged as a successful method for
unsupervised graph representation learning. Most graph CL methods first perform
stochastic augmentation on the input graph to obtain two graph views and
maximize the agreement of representations in the two views. Despite the
prosperous development of graph CL methods, the design of graph augmentation
schemes -- a crucial component in CL -- remains rarely explored. We argue that
the data augmentation schemes should preserve intrinsic structures and
attributes of graphs, which will force the model to learn representations that
are insensitive to perturbation on unimportant nodes and edges. However, most
existing methods adopt uniform data augmentation schemes, like uniformly
dropping edges and uniformly shuffling features, leading to suboptimal
performance. In this paper, we propose a novel graph contrastive representation
learning method with adaptive augmentation that incorporates various priors for
topological and semantic aspects of the graph. Specifically, on the topology
level, we design augmentation schemes based on node centrality measures to
highlight important connective structures. On the node attribute level, we
corrupt node features by adding more noise to unimportant node features, to
enforce the model to recognize underlying semantic information. We perform
extensive experiments of node classification on a variety of real-world
datasets. Experimental results demonstrate that our proposed method
consistently outperforms existing state-of-the-art baselines and even surpasses
some supervised counterparts, which validates the effectiveness of the proposed
contrastive framework with adaptive augmentation.

本文提出了一种新的自适应增强方法，通过设计基于节点中心性和节点属性的增强策略来保留图的内在结构和属性信息，并验证此方法在节点分类任务中优于现有方法和监督学习模型。