Work

Adaptive imbalanced node classification graph contrastive learning

Year: 2025

Type: article

Abstract: Graph Contrastive Learning (GCL) is a powerful self-supervised technique for learning node and graph representations. However, real-world graph data often exhibit imbalanced class distributions, which pose significant challenges to GCL’s effectiveness. Our experiments show that current state-of-the-art (SOTA) methods perform poorly under imbalanced settings. To address this, we propose a novel GCL framework called AIGCL for imbalanced node classification. This framework automatically and adaptively balances the node representations learned by GCL. Specifically, we introduce a new data augmentation method that retains more information from minority class nodes during graph augmentation. Additionally, we use an imbalance rate adaptive sampling strategy to balance the data. We also incorporate a Variational Graph Autoencoder (VGAE) with an encoder–decoder structure to pretrain the data and generate high-quality pseudo-labels. Our experiments demonstrate that under imbalanced settings, our model improves classification accuracy by 4 %-12 % compared to baseline models, significantly enhancing the performance of minority class nodes.

Source: Neurocomputing

DOI: 10.1016/j.neucom.2025.131280

Authors: Xinyu Hu, Donghai Guan, Weiwei Yuan, Qi Zhu, Çetin Kaya Koç

Institution: Nanjing University of Aeronautics and Astronautics

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: hybrid

Estimated APC paid: $2,670