Title: One for Two: A Unified Framework for Imbalanced Graph Classification via Dynamic Balanced Prototype
Primary area: learning on graphs and other geometries & topologies
Keywords: Graph classification; graph imbalance learning; graph neural networks; Graph data mining; long-tail learning

Abstract: Graph Neural Networks (GNNs) have advanced graph classification, yet they remain vulnerable to graph-level imbalance, encompassing class imbalance and topological imbalance. To address both types of imbalance in a unified manner, we propose UniImb, a novel framework for imbalanced graph classification. Specifically, UniImb first captures multi-scale topological features and enhances data diversity via learnable personalized graph perturbations. It then employs a dynamic balanced prototype module to learn representative prototypes from graph instances, improving the quality of graph representations. Concurrently, a prototype load-balancing optimization term mitigates dominance by majority samples to equalize sample influence during training. We justify these design choices theoretically using the Information Bottleneck principle. Extensive experiments on 13 datasets and 20 baselines demonstrate that UniImb substantially improves graph classification under diverse imbalance scenarios, achieving up to 41.62% improvement on class-imbalance tasks and up to 20.57% improvement on topological-imbalance tasks. Our code is available at Anonymous GitHub.