Gehang Zhang

Noise-Disentangled Graph Contrastive Learning via Low-Rank and Sparse Subspace Decomposition

Graph contrastive learning aims to learn a representative model by maximizing the agreement between different views of the same graph. Existing studies usually allow multifarious noise in data augmentation, and suffer from trivial and inconsistent generation of graph views. Moreover, they mostly imp…