  Graph contrastive learning (GCL) aims to contrast positive-negative
counterparts to learn the node embeddings, whereas graph data augmentation
methods are employed to generate these positive-negative samples. The
variation, quantity, and quality of negative samples compared to positive
samples play crucial roles in learning meaningful embeddings for node
classification downstream tasks. Less variation, excessive quantity, and
low-quality negative samples cause the model to be overfitted for particular
nodes, resulting in less robust models. To solve the overfitting problem in the
GCL paradigm, this study proposes a novel Cumulative Sample Selection (CSS)
algorithm by comprehensively considering negative samples' quality, variations,
and quantity. Initially, three negative sample pools are constructed: easy,
medium, and hard negative samples, which contain 25%, 50%, and 25% of the total
available negative samples, respectively. Then, 10% negative samples are
selected from each of these three negative sample pools for training the model.
After that, a decision agent module evaluates model training results and
decides whether to explore more negative samples from three negative sample
pools by increasing the ratio or keep exploiting the current sampling ratio.
The proposed algorithm is integrated into a proposed graph contrastive learning
framework named NegAmplify. NegAmplify is compared with the SOTA methods on
nine graph node classification datasets, with seven achieving better node
classification accuracy with up to 2.86% improvement.
