NodeImport: Imbalanced Node Classification with Node Importance Assessment

2026-07-15

AI Summary

This work addresses the challenge of class imbalance in graph node classification, where models tend to favor majority classes. To mitigate this bias, the authors propose a general-purpose framework that decouples node generation from selection. The method constructs a high-quality meta-set that approximates the global feature distribution, enabling efficient estimation of node importance. It dynamically selects labeled, unlabeled, and synthetically generated nodes whose inclusion yields significant gains in unbiased performance. Theoretical analysis supports the efficiency of the importance computation, and by integrating graph neural networks with meta-learning principles, the framework achieves fine-grained node selection. Extensive experiments demonstrate that the proposed approach consistently outperforms state-of-the-art methods across multiple datasets, effectively alleviating class imbalance and substantially improving overall classification performance.

Abstract

In real-world applications, node classification on graphs often faces the challenge of class imbalance, where majority classes dominate training, resulting in biased model performance. Traditional GNNs often struggle in such scenarios, as they tend to overfit to majority classes while underrepresenting minority classes. Existing solutions, which either prioritize nodes based on class size or synthesize new nodes for minority classes, often fall short of effectively addressing this imbalance issue. This paper introduces an approach to class-imbalanced node classification by utilizing a balanced meta-set for importance measurement, where a training node is considered significant if it enhances model performance under an unbiased setting. Our method identifies important nodes that can counteract class imbalance and utilizes them for model training, allowing for fine-grained and dynamic node selection throughout the training process. We theoretically derive a formula to directly assess node importance, reducing computational overhead and providing an intuitive threshold for node selection. Guided by this metric, we develop a novel framework that filters valuable labeled, unlabeled, and synthetic nodes that enhance model performance in an unbiased context. A key advantage of this framework is its separation of the synthetic node generation process from the filtering process, ensuring compatibility with various node generation methods. Furthermore, we introduce a strategy to construct a high-quality meta-set that closely approximates the overall feature distribution, ensuring robust representation of each class. We evaluate our framework, NodeImport, across multiple datasets using popular GNN architectures, demonstrating its superiority over existing baselines. Our results highlight the flexibility and effectiveness of the framework in mitigating class imbalance, leading to improved outcomes.

Problem

• class imbalance
• node classification
• graph neural networks
• minority classes
• imbalanced learning

Innovation

• imbalanced node classification
• node importance assessment
• meta-set
• graph neural networks
• synthetic node filtering