Graph contrastive learning (GCL) has emerged as a representative graph self-supervised method, achieving significant success. The currently prevalent optimization objective for GCL is InfoNCE. Typically, it employs augmentation techniques to obtain two views, where a node in one view acts as the anchor, the corresponding node in the other view serves as the positive sample, and all other nodes are regarded as negative samples. The goal is to minimize the distance between the anchor node and positive samples and maximize the distance to negative samples. However, due to the lack of label information during training, InfoNCE inevitably treats samples from the same class as negative samples, leading to the issue of false negative samples. This can impair the learned node representations and subsequently hinder performance in downstream tasks. While numerous methods have been proposed to mitigate the impact of false negatives, they still face various challenges. For instance, while increasing the number of negative samples can dilute the impact of false negatives, it concurrently increases computational burden. Thus, we propose GraphRank, a simple yet efficient graph contrastive learning method that addresses the problem of false negative samples by redefining the concept of negative samples to a certain extent, thereby avoiding the issue of false negative samples. The effectiveness of GraphRank is empirically validated through experiments on the node, edge, and graph level tasks.

翻译：图对比学习（Graph Contrastive Learning, GCL）已成为一种代表性的图自监督方法，并取得了显著成功。当前GCL的主流优化目标为InfoNCE。通常，它采用数据增强技术获取两个视图，其中一个视图中的节点作为锚点，另一视图中对应的节点作为正样本，而所有其他节点被视为负样本。其目标是最小化锚点与正样本之间的距离，并最大化与负样本之间的距离。然而，由于训练过程中缺乏标签信息，InfoNCE不可避免地会将同一类别的样本视为负样本，从而导致假负样本问题。这会损害学习到的节点表示，进而阻碍下游任务的性能。尽管已有许多方法被提出以减轻假负样本的影响，但它们仍面临各种挑战。例如，增加负样本数量虽能稀释假负样本的影响，却同时增加了计算负担。为此，我们提出GraphRank——一种简单高效的图对比学习方法，通过在一定程度上重新定义负样本的概念来解决假负样本问题，从而避免该问题的产生。GraphRank的有效性通过节点级、边级和图级任务的实验得到了实证验证。