G2CL: Gradient-guided graph contrastive learning for eliminating the message contrastive conflict

Graph contrastive learning methods based on the information noise contrastive estimation (InfoNCE) loss have made significant advances in graph representation learning. However, existing methods primarily focus on optimizing graph augmentation strategies or contrastive objectives. They cannot effectively eliminate the message contrastive conflict (MCC) that arises from the collaboration between the InfoNCE loss and the message-passing mechanism of graph neural networks. The MCC prevents the effective minimization of similarity among negative samples, thereby undermining the efficacy of graph contrastive learning. Furthermore, the issues of false negative samples and long-tail conflict effect (LCE) under the MCC remain unresolved. To this end, a novel method termed gradient-guided graph contrastive learning for eliminating the message contrastive conflict (G2CL) is proposed. First, this study theoretically demonstrates the existence of the MCC and analyzes in detail the impact of false negative samples and LCE on the MCC. In addition, a new gradient-guided dynamic capturer is proposed to eliminate the MCC. Next, based on the semantic and topological information of the graph, a new false negative strategy is proposed to address the issue of false negative samples. Furthermore, a new pheromone-based message-passing mechanism is proposed to address the issue of LCE. Finally, extensive experiments on 11 datasets demonstrate that the G2CL outperforms state-of-the-art baselines.