Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift

TL;DR

This paper introduces a novel graph contrastive learning method that employs topology-based augmentations and prototype-based negative sampling to enhance node representations and mitigate semantic drift.

Contribution

The paper proposes two global topological augmentations and a prototype-based negative pair selection to improve GCL effectiveness and reduce semantic drift.

Findings

Enhanced node representations with improved diversity.

Reduced semantic drift through prototype-based negative sampling.

Outperforms state-of-the-art methods on various tasks.

Abstract

Graph contrastive learning (GCL) is an effective paradigm for node representation learning in graphs. The key components hidden behind GCL are data augmentation and positive-negative pair selection. Typical data augmentations in GCL, such as uniform deletion of edges, are generally blind and resort to local perturbation, which is prone to producing under-diversity views. Additionally, there is a risk of making the augmented data traverse to other classes. Moreover, most methods always treat all other samples as negatives. Such a negative pairing naturally results in sampling bias and likewise may make the learned representation suffer from semantic drift. Therefore, to increase the diversity of the contrastive view, we propose two simple and effective global topological augmentations to compensate current GCL. One is to mine the semantic correlation between nodes in the feature space. The other is to utilize the algebraic properties of the adjacency matrix to characterize the topology by eigen-decomposition. With the help of both, we can retain important edges to build a better view. To reduce the risk of semantic drift, a prototype-based negative pair selection is further designed which can filter false negative samples. Extensive experiments on various tasks demonstrate the advantages of the model compared to the state-of-the-art methods.