Graph Contrastive Learning (GCL) is an effective way to learn generalized
graph representations in a self-supervised manner, and has grown rapidly in
recent years. However, the underlying community semantics has not been well
explored by most previous GCL methods. Research that attempts to leverage
communities in GCL regards them as having the same influence on the graph,
leading to extra representation errors. To tackle this issue, we define
''community strength'' to measure the difference of influence among
communities. Under this premise, we propose a Community-Strength-enhanced Graph
Contrastive Learning (CSGCL) framework to preserve community strength
throughout the learning process. Firstly, we present two novel graph
augmentation methods, Communal Attribute Voting (CAV) and Communal Edge
Dropping (CED), where the perturbations of node attributes and edges are guided
by community strength. Secondly, we propose a dynamic ''Team-up'' contrastive
learning scheme, where community strength is used to progressively fine-tune
the contrastive objective. We report extensive experiment results on three
downstream tasks: node classification, node clustering, and link prediction.
CSGCL achieves state-of-the-art performance compared with other GCL methods,
validating that community strength brings effectiveness and generality to graph
representations. Our code is available at
this https URL

为了解决 GCL 方法中的 community semantics 问题，我们提出了一种称为 CSGCL 的 algorithmic framework，并使用两种新的图扩充方法和动态的 'Team-up' 对比学习策略，传播 community strength，实现了对图表示的有效性和通用性的验证。