Contrastive Multi-view Learning for Graph Stucture Learning

Abstract

Graph Structure Learning (GSL), with its ability to simultaneously optimize the graph structure and learn the suitable parameters of Graph Neural Networks (GNN), has attracted considerable attention. Although existing methods are capable of learning optimal graph structure from single or multiple information sources, they exhibit certain limitations such as disregarding intricate relational information in the original graph structure and bias introduced by a small number of labels. In this study, we introduce a novel contrastive multi-view method for graph structure learning, named CMVGSL, which estimates graph structure suited for GNN properties from a broader range of perspectives. Specifically, we extract a k truss subgraph as an augmented view for contrastive learning. The representations learned from both semi-supervised and contrastive learning are utilized to construct observations of the optimal graph, and then the estimated graph structure is obtained by employing a Bayesian inference-based method. Furthermore, we design a post-processing method to impose sparsity and internode distance constraints on the estimated graph. The joint optimization of graph structure and GNN parameters is achieved through multiple iterations. Extensive experimental results on benchmark datasets with different homophily demonstrate the significant effectiveness of our proposed CMVGSL.