Graph contrastive learning based on neighbor supervision and adaptive enhancement
In response to the issues of random graph augmentation and the omission of considering graph homogeneity in the loss function in most graph contrastive learning methods,a graph contrastive learning framework based on neighbor supervision and adaptive enhancement is proposed.The framework utilizes the centrality of node feature vectors in the input graph for adaptive enhancement,generating two views that prevent the deletion of important nodes and edges caused by random augmentation.The framework learns are guided by the neighbor supervised graph contrastive loss function tailored for graph-structured data,using network topology as a supervisory signal to define positive and negative samples in contrastive learning,allowing multiple positive samples for each anchor point.The proposed framework is evaluated through node classification experiments on three citation datasets,and the experimental results demonstrate its superior performance in terms of classification accuracy compared to several baseline methods.