Generalized Few-Shot Node Classification via Training Set Refinement

Yayong Li, Xubo Zhang, Hong Zhang, Nan Ye, Zongli Liu, Jinran Wu

Abstract: Few-shot node classification is well studied to transfer knowledge from base classes to novel classes for rapid adaptation. However, traditional few-shot learning approaches often assume a disjoint class setting, which does not align with real-world scenarios. In this work, we focus on the Generalized Few-Shot Node Classification (GFSNC) problem, where the inference is required on both base and novel classes as in many real-world scenarios, which remains an open challenge currently. The core challenge in GFSNC lies in the distribution shift between training and test data, which arises from two main factors: (1) the training set is highly imbalanced between base and novel classes, not aligning with the test set; (2) supervisions for novel classes are extremely sparse, while the underlying class distributions are often complex. To address these challenges, we propose to refine the training set to better approach the distribution of the test. Specifically, to mitigate class imbalance, we construct a small and compact graph as a proxy of the original graph through distribution matching, which balances training data across base and novel classes. Furthermore, to better approximate the true distribution of novel classes, we introduce a structure-aware node augmentation strategy to calibrate the novel prototypes and enhance their representativeness. Extensive experiments on four benchmark datasets demonstrate that the proposed method consistently outperforms state-of-the-art baselines in the GFSNC setting.

External IDs:doi:10.1007/978-981-95-7075-1_32