Smoothed Graph Contrastive Learning via Seamless Proximity Integration
Maysam Behmanesh Maks Ovsjanikov
Graph contrastive learning (GCL) aligns node representations by classifying
node pairs into positives and negatives using a selection process that
typically relies on establishing correspondences within two augmented graphs.
The conventional GCL approaches incorporate negative samples uniformly in the
contrastive loss, resulting in the equal treatment negative nodes, regardless
of their proximity to the true positive. In this paper, we present a Smoothed
Graph Contrastive Learning model (SGCL), which leverages the geometric
structure of augmented graphs to inject proximity information associated with
positive/negative pairs in the contrastive loss, thus significantly
regularizing the learning process. The proposed SGCL adjusts the penalties
associated with node pairs in the contrastive loss by incorporating three
distinct smoothing techniques that result in proximity aware positives and
negatives. To enhance scalability for large-scale graphs, the proposed
framework incorporates a graph batch-generating strategy that partitions the
given graphs into multiple subgraphs, facilitating efficient training in
separate batches. Through extensive experimentation in the unsupervised setting
on various benchmarks, particularly those of large scale, we demonstrate the
superiority of our proposed framework against recent baselines.