NodeReg: Mitigating the Imbalance and Distribution Shift Effects in Semi-Supervised Node Classification via Norm Consistency

2025-03-05

AI Summary

To address the degradation of graph neural network (GNN) generalization in semi-supervised node classification caused by neighborhood class imbalance and distribution shift, this paper identifies, for the first time, that norm consistency of node representations jointly mitigates both issues. Building on this insight, we propose NodeReg—a lightweight, unsupervised regularization method that enforces Lipschitz continuity on node representation norms, requiring neither additional labels nor data augmentation. NodeReg is seamlessly integrable into mainstream GNN architectures such as GCN. Extensive experiments across five benchmark datasets demonstrate its effectiveness: under class imbalance, it improves macro-F1 by 1.4–25.9 percentage points; under distribution shift, it boosts accuracy by 1.4–3.1 percentage points—consistently outperforming state-of-the-art methods.

Technology Category

• Machine Learning: Semi-Supervised Learning
• Natural Language Processing: Safety and Robustness
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Aggregating information from neighboring nodes benefits graph neural networks (GNNs) in semi-supervised node classification tasks. Nevertheless, this mechanism also renders nodes susceptible to the influence of their neighbors. For instance, this will occur when the neighboring nodes are imbalanced or the neighboring nodes contain noise, which can even affect the GNN's ability to generalize out of distribution. We find that ensuring the consistency of the norm for node representations can significantly reduce the impact of these two issues on GNNs. To this end, we propose a regularized optimization method called NodeReg that enforces the consistency of node representation norms. This method is simple but effective and satisfies Lipschitz continuity, thus facilitating stable optimization and significantly improving semi-supervised node classification performance under the above two scenarios. To illustrate, in the imbalance scenario, when training a GCN with an imbalance ratio of 0.1, NodeReg outperforms the most competitive baselines by 1.4%-25.9% in F1 score across five public datasets. Similarly, in the distribution shift scenario, NodeReg outperforms the most competitive baseline by 1.4%-3.1% in accuracy.

Problem

• Mitigates imbalance effects in semi-supervised node classification.
• Reduces distribution shift impact on graph neural networks.
• Improves node classification via norm consistency regularization.

Innovation

• NodeReg enforces norm consistency in node representations.
• NodeReg mitigates imbalance and distribution shift effects.
• NodeReg improves GNN performance in semi-supervised classification.