  Graph Neural Networks (GNNs) have received extensive research attention due
to their powerful information aggregation capabilities. Despite the success of
GNNs, most of them suffer from the popularity bias issue in a graph caused by a
small number of popular categories. Additionally, real graph datasets always
contain incorrect node labels, which hinders GNNs from learning effective node
representations. Graph contrastive learning (GCL) has been shown to be
effective in solving the above problems for node classification tasks. Most
existing GCL methods are implemented by randomly removing edges and nodes to
create multiple contrasting views, and then maximizing the mutual information
(MI) between these contrasting views to improve the node feature
representation. However, maximizing the mutual information between multiple
contrasting views may lead the model to learn some redundant information
irrelevant to the node classification task. To tackle this issue, we propose an
effective Contrastive Graph Representation Learning with Adversarial Cross-view
Reconstruction and Information Bottleneck (CGRL) for node classification, which
can adaptively learn to mask the nodes and edges in the graph to obtain the
optimal graph structure representation. Furthermore, we innovatively introduce
the information bottleneck theory into GCLs to remove redundant information in
multiple contrasting views while retaining as much information as possible
about node classification. Moreover, we add noise perturbations to the original
views and reconstruct the augmented views by constructing adversarial views to
improve the robustness of node feature representation. Extensive experiments on
real-world public datasets demonstrate that our method significantly
outperforms existing state-of-the-art algorithms.
