Yue Yu, Xiao Wang, Mengmei Zhang, Nian Liu, Chuan Shi
TL;DR通过提出节点紧凑度作为度量,证明了基于图对比学习在训练过程中存在不平衡问题,并推导了节点紧凑度的理论形式,提出了一种能够更好地遵循基于图对比学习原则的 PrOvable Training (POT) 方法,在多个基准测试中持续提升了现有的基于图对比学习方法。
Abstract
graph contrastive learning (GCL) has emerged as a popular training approach for learning node embeddings from augmented graphs without labels. Despite the key principle that maximizing the similarity between posi