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.

本研究提出了一种新的 Cumulative Sample Selection (CSS) 算法，将其集成到名为 NegAmplify 的图对比学习框架中，通过综合考虑负样本的质量、变化和数量来解决图对比学习范式中的过拟合问题，在九个图节点分类数据集上与先进方法进行了比较，其中七个数据集的节点分类准确性提高了最多 2.86%。

从过拟合到稳健性：基于数量、质量和多样性的负样本选择在图对比学习中的应用

From Overfitting to Robustness: Quantity, Quality, and Variety Oriented  Negative Sample Selection in Graph Contrastive Learning

The task of graph node classification is often approached by utilizing a
local Graph Neural Network (GNN), that learns only local information from the
node input features and their adjacency. In this paper, we propose to improve
the performance of node classification GNNs by utilizing both global and local
information, specifically by learning label- and node- features. We therefore
call our method Global-Local-GNN (GLGNN). To learn proper label features, for
each label, we maximize the similarity between its features and nodes features
that belong to the label, while maximizing the distance between nodes that do
not belong to the considered label. We then use the learnt label features to
predict the node classification map. We demonstrate our GLGNN using three
different GNN backbones, and show that our approach improves baseline
performance, revealing the importance of global information utilization for
node classification.

提出了一种名为 Global-Local-GNN (GLGNN) 的方法，通过利用全局和局部信息以及节点标签和特征的学习，提高了节点分类 GNNs 的性能，并显示了全局信息利用对节点分类的重要性。