GraphVCM: Virtual Center Mixing with Distance-Aware Regulation for Class Imbalanced Node Classification

Abstract

Class imbalance is a prevalent issue in real-world graph-structure data, such as social and citation networks, posing significant challenges for Graph Neural Networks (GNNs). Existing solutions often focus on balancing class distributions via oversampling techniques, which may lead to overfitting and blurred decision boundaries between majority and minority classes. To address this issue, we propose GraphVCM, a novel virtual center mixing with distance-aware regulation approach for class imbalanced node classification. GraphVCM first leverages spectral clustering to generate virtual center nodes, representing the features of majority class samples. These virtual centers are then mixed with minority class nodes to synthesize new nodes for enhancing minority class representation. Moreover, we introduce a distance-aware regulation module to optimize the inter-class decision boundaries by simultaneously maximizing inter-class distances and minimizing intra-class distances. Extensive experiments on multiple public datasets demonstrate that GraphVCM consistently outperforms state-of-the-art methods and effectively alleviates the negative impact of class imbalance.