Perfect Alignment May be Poisonous to Graph Contrastive Learning
Jingyu Liu Huayi Tang Yong Liu
Graph Contrastive Learning (GCL) aims to learn node representations by
aligning positive pairs and separating negative ones. However, limited research
has been conducted on the inner law behind specific augmentations used in
graph-based learning. What kind of augmentation will help downstream
performance, how does contrastive learning actually influence downstream tasks,
and why the magnitude of augmentation matters? This paper seeks to address
these questions by establishing a connection between augmentation and
downstream performance, as well as by investigating the generalization of
contrastive learning. Our findings reveal that GCL contributes to downstream
tasks mainly by separating different classes rather than gathering nodes of the
same class. So perfect alignment and augmentation overlap which draw all
intra-class samples the same can not explain the success of contrastive
learning. Then in order to comprehend how augmentation aids the contrastive
learning process, we conduct further investigations into its generalization,
finding that perfect alignment that draw positive pair the same could help
contrastive loss but is poisonous to generalization, on the contrary, imperfect
alignment enhances the model's generalization ability. We analyse the result by
information theory and graph spectrum theory respectively, and propose two
simple but effective methods to verify the theories. The two methods could be
easily applied to various GCL algorithms and extensive experiments are
conducted to prove its effectiveness.