Neighbor Contrastive Learning on Learnable Graph Augmentation

Understand

Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress.

• However, the existing GCL methods mostly adopt human-designed graph augmentations, which are sensitive to various graph datasets.
• In addition, the contrastive losses originally developed in computer vision have been directly applied to graph data, where the neighboring nodes are regarded as negatives and consequently pushed far apart from the anchor.
• However, this is contradictory with the homophily assumption of networks that connected nodes often belong to the same class and should be close to each other.