IsGCL: Informative Sample-Aware Progressive Graph Contrastive Learning

Graph-level Contrastive Learning (GCL) has evolved as a powerful technique to derive representations from contrastive view pairs. Without access to labeled data, GCL typically takes two views augmented from the same graph as a positive pair and embeds them in nearby locations, while treating views from different graphs as negative pairs and pushing away their representations. Since the construction of contrastive pairs plays an important role in GCL, considerable attention has been paid to informative pairs mining. However, existing informative pairs mining methods suffer from the following two challenges: 1) Previous studies merely pay attention to the informative negative pairs while neglecting the informative positive pairs. Nevertheless, most augmentation methods require random perturbations, which may destroy the critical semantics of a graph, leading to false positive pairs (uninformative positives). 2) For informative negatives mining, most existing studies either overly emphasize hard negatives despite their potential unreliability, or rely on precise clustering pseudo-labels, which are error-prone especially in the early training stage. To solve the above challenges, we propose an informative sample-aware progressive graph contrastive learning framework, which filters both uninformative positives and negatives. In particular, we first present a progressive views sampler to evaluate the learning hardness of each view via clustering. Then, we feed model views with appropriate hardness, meaning those that aren't too challenging for the current model to assign pseudo labels confidently. Furthermore, we propose two samplers to filter out uninformative positives and negatives, respectively. Empirical results demonstrate the efficacy of our method IsGCL, which outperforms baselines by a margin of 2.5% on both MUTAG and PTC-MR in unsupervised learning settings. Furthermore, IsGCL maintains competitive training efficiency11Code available at https://github.com/jxzeng-git/IsGCL.