FDAGCL:Feature Discrepancy-Aware Graph Contrastive Learning

In recent years, Graph Contrastive Learning (GCL) has emerged as a key research direction for learning representations of unlabeled graph data, focusing on the self-supervised learning of efficient representations for both graphs and nodes. However, existing approaches typically assume feature homogeneity across different augmented views, overlooking the potential impact of inter-view feature differences, particularly weak features, on model performance. To address the problem of weak features and the feature differences between different enhanced views, this paper proposes the Feature Discrepancy-Aware Graph Contrastive Learning (FDAGCL) framework. Firstly, FDAGCL dynamically adjusts the importance of features through the feature importance decoupling mechanism, thereby effectively distinguishing strong features from weak view. Secondly, FDAGCL designs a multi-view map contrastive learning strategy to enhance the expression of strong features while simultaneously improving the learning of weak features through strong-strong view and strong-weak view map contrastive learning, thereby achieving information complementarity. To validate the effectiveness of our method, we conducted extensive empirical experiments on five datasets. The results demonstrate that FDAGCL exhibits significant superiority over the baseline methods.