Perfect Alignment May be Poisonous to Graph Contrastive Learning
Jingyu Liu, Huayi Tang, Yong Liu
TL;DR
This paper investigates how data augmentation influences graph contrastive learning, revealing that perfect alignment can harm generalization and that carefully designed augmentations are essential for optimal downstream performance.
Contribution
It uncovers the nuanced role of augmentation in GCL, showing that perfect alignment may not always improve downstream tasks and proposing methods to analyze and verify these effects.
Findings
Perfect alignment draws all intra-class samples together but may harm generalization.
Designed augmentation improves downstream accuracy over perfect alignment.
Proposed methods are effective and applicable to various GCL algorithms.
Abstract
Graph Contrastive Learning (GCL) aims to learn node representations by aligning positive pairs and separating negative ones. However, few of researchers have focused 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 so much? This paper seeks to address these questions by establishing a connection between augmentation and downstream performance. 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 fully explain the success of contrastive learning. Therefore, in order to understand how augmentation aids…
Peer Reviews
Decision·ICML 2024 Poster
The relationships between augmentations, contrastive training and generation is a valuable topic; The experiments are sufficient; The analysis is informative (though sometimes confusing in writing).
**W1.** Lack of related work. I note that you positioned the related work in the appendix. However, given that you mentioned other works (like Wang et al. 2022b, Saunshi et al. 2022) many times in the paper, an explicit and focused discussion on the differences between your work and others is expected. **W2.** Some definitions are unclear, resulting in harder understanding. For example, 1) *augmentation overlap.* You give an intuitive explanation as “support overlap between different intra-cl
1) The paper is on an interesting topic, and through extensive experiments, the authors show that the proposed theories can improve the performance of existing contrastive learning models in most cases. 2) The paper is well-written and easy to follow. The structure of the paper is very good, the authors describe the related works and the existing challenges and limitations. Then they describe their theories and then the provide with the theoretical background. At the end, they provide well desig
1) The novelty of the proposed methodology is limited. 2) Some details on the experiments are missing. For example, what are the sizes of the datasets/train sets/validation sets/test sets? Are the reported performances tested for statistical significance? These are important details that will help the reader to be more convinced and make a stronger point on the improvement of the performances. 3) Another important detail on the experiments would be to add the run times, so that the reader would
S1: The study focuses on the influence of alignment in Graph Contrastive Learning. It emphasizes that the Graph Contrastive Learning (GCL) method significantly contributes to downstream tasks by effectively discerning between various classes, rather than consolidating nodes of the same class. S2: This challenges the traditional assumption that perfect alignment and augmented overlap is beneficial in contrastive learning. In an effort to delve deeper into the role of augmentation in the contras
W1: Theorem 2.5 explains why as the augmentation becomes stronger, negative center similarity decreases, (The answer is simultaneously we also want the inner class samples to be closer, and there is a tension between these two objectives. But this is almost always expected regardless of theorem 2.5). This does not mean that pulling closer the augmentations is completely harmful, as there is potentially a sweet point between pulling closer inner class samples and pushing away inter class samples
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Taxonomy
TopicsAdvanced Graph Neural Networks · Topic Modeling
MethodsContrastive Learning