Boosting graph contrastive learning via adaptive graph augmentation and topology-feature-level homophily

Graph contrastive learning, which aims to learn supervised signals from unlabeled graph data, has gained popularity as an effective method for learning node representations. However, most existing methods leverage random edge dropping to obtain the augmented view, which results in many isolated nodes and leads to limited performance. Moreover, how to reasonably and accurately identify important topology-feature-level positive samples with graph homophily is still an interesting and challenging problem. To address these issues, we propose a novel graph contrastive learning method with adaptive graph augmentation and topology-feature-level homophily, named GCL-GATH. Specifically, GCL-GATH assigns different weights to edges during the graph augmentation process, aiming to preserve the global topological structure as much as possible. Moreover, it simultaneously utilizes both structural and feature information to select positive samples from neighboring nodes. Extensive experimental results fully demonstrate that the proposed GCL-GATH outperforms the state-of-the-art methods. The source codes of this work are available at https://github.com/ZZY-GraphMiningLab/GCL-GATH.