Graph representation learning has long been an important yet challenging task for various real-world applications. However, their downstream tasks are mainly performed in the settings of supervised or semi-supervised learning. Inspired by recent advances in unsupervised contrastive learning, this paper is thus motivated to investigate how the node-wise contrastive learning could be performed. Particularly, we respectively resolve the class collision issue and the imbalanced negative data distribution issue. Extensive experiments are performed on three real-world datasets and the proposed approach achieves the SOTA model performance.

翻译：长期以来,图表代表学习一直是各种现实世界应用中一项重要但具有挑战性的任务,然而,其下游任务主要是在受监督或半受监督学习环境中完成的。在不受监督对比学习的最新进展的启发下,本文件因此积极调查如何开展节点对立学习。特别是,我们分别解决了阶级碰撞问题和不平衡的负面数据分布问题。对三个现实世界数据集进行了广泛的实验,拟议的方法实现了SOTA模型的性能。