Hongshan Pu

Graph Contrastive Learning with Balanced Hard Negatives and Fine-grained Semantic-aware Positives

Graph contrastive learning (GCL) aims to learn representations by bringing semantically similar graphs closer and pushing dissimilar ones farther apart without label supervision. Hard negatives, which refer to graphs that have different labels but similar embeddings to the target graph, play a key r