Curriculum-guided dynamic division strategy for graph contrastive learning

Abstract

Contrastive learning is a commonly used framework in the field of graph self-supervised learning, where models are trained by bringing positive samples closer together and pushing negative samples apart. Most existing graph contrastive learning models divide all nodes into positive and negative samples, which leads to the selection of some meaningless samples and reduces the model’s performance. Additionally, there is a significant disparity in the ratio between positive and negative samples, with an excessive number of negative samples introducing noise. Therefore, we propose a novel dynamic sampling strategy that selects more meaningful samples from the perspectives of structure and features and we incorporate an iteration-based sample selection process into the model training to enhance its performance. Furthermore, we introduce a curriculum learning training method based on the principle of starting from easy to difficult. Sample training for each iteration is treated as a task, enabling the rapid capture of relevant and meaningful sample information. Extensive experiments have been conducted to validate the superior performance of our model across nine real-world datasets.