Multi-view Hierarchical Graph Contrastive Learning based on Asynchronous Asymmetric Structure

Contrastive learning has strong generalization ability and the capability to learn automatically without labeled information. However, it still faces challenges such as insufficient feature diversity, a lack of multi-level semantics, and the balance between tolerance and consistency. To address these challenges, This study propose a Multi-view Hierarchical Graph Contrastive Learning method. First, a new view is generated through a diffusion matrix to provide multi-view data for contrastive learning. Then, these multi-view data are fed into an asynchronous asymmetric network structure, specifically using graph network models to learn diversified features. Next, we adopt a self-designed hierarchical contrastive learning framework, constructing a three-level contrastive loss for joint optimization of nodes, subgraphs, and global graphs. Meanwhile, we introduce alignment and consistency and appropriately adjust the loss function through a temperature coefficient. Ultimately, the model achieves excellent classification performance on multiple datasets through node classification and graph classification tasks.