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GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance

Chaofan Zhu, Xiaobing Rui, Zhixiao Wang

TL;DR

This work tackles imbalanced node classification by showing that structural imbalance in graphs biases GNNs toward majority classes and diminishes minority signals. It introduces GraphSB, a two-stage framework consisting of Structure Enhancement and Relation Diffusion to rebalance graph structure before node synthesis, plus an adaptive oversampling mechanism. The authors provide theoretical insights into how structural bias arises and demonstrate, through extensive experiments on five datasets, that GraphSB achieves state-of-the-art performance and can serve as a plug-in module for other synthesis-based methods. The approach yields robust improvements across varying imbalance ratios and even on synthetic networks, highlighting its practical impact for fairer and more accurate graph learning.

Abstract

Imbalanced node classification is a critical challenge in graph learning, where most existing methods typically utilize Graph Neural Networks (GNNs) to learn node representations. These methods can be broadly categorized into the data-level and the algorithm-level. The former aims to synthesize minority-class nodes to mitigate quantity imbalance, while the latter tries to optimize the learning process to highlight minority classes. However, neither category addresses the inherently imbalanced graph structure, which is a fundamental factor that incurs majority-class dominance and minority-class assimilation in GNNs. Our theoretical analysis further supports this critical insight. Therefore, we propose GraphSB (Graph Structural Balance), a novel framework that incorporates Structural Balance as a key strategy to address the underlying imbalanced graph structure before node synthesis. Structural Balance performs a two-stage structure optimization: Structure Enhancement that adaptively builds similarity-based edges to strengthen connectivity of minority-class nodes, and Relation Diffusion that captures higher-order dependencies while amplifying signals from minority classes. Thus, GraphSB balances structural distribution before node synthesis, enabling more effective learning in GNNs. Extensive experiments demonstrate that GraphSB significantly outperforms the state-of-the-art methods. More importantly, the proposed Structural Balance can be seamlessly integrated into state-of-the-art methods as a simple plug-and-play module, increasing their accuracy by an average of 3.67\%.

GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance

TL;DR

This work tackles imbalanced node classification by showing that structural imbalance in graphs biases GNNs toward majority classes and diminishes minority signals. It introduces GraphSB, a two-stage framework consisting of Structure Enhancement and Relation Diffusion to rebalance graph structure before node synthesis, plus an adaptive oversampling mechanism. The authors provide theoretical insights into how structural bias arises and demonstrate, through extensive experiments on five datasets, that GraphSB achieves state-of-the-art performance and can serve as a plug-in module for other synthesis-based methods. The approach yields robust improvements across varying imbalance ratios and even on synthetic networks, highlighting its practical impact for fairer and more accurate graph learning.

Abstract

Imbalanced node classification is a critical challenge in graph learning, where most existing methods typically utilize Graph Neural Networks (GNNs) to learn node representations. These methods can be broadly categorized into the data-level and the algorithm-level. The former aims to synthesize minority-class nodes to mitigate quantity imbalance, while the latter tries to optimize the learning process to highlight minority classes. However, neither category addresses the inherently imbalanced graph structure, which is a fundamental factor that incurs majority-class dominance and minority-class assimilation in GNNs. Our theoretical analysis further supports this critical insight. Therefore, we propose GraphSB (Graph Structural Balance), a novel framework that incorporates Structural Balance as a key strategy to address the underlying imbalanced graph structure before node synthesis. Structural Balance performs a two-stage structure optimization: Structure Enhancement that adaptively builds similarity-based edges to strengthen connectivity of minority-class nodes, and Relation Diffusion that captures higher-order dependencies while amplifying signals from minority classes. Thus, GraphSB balances structural distribution before node synthesis, enabling more effective learning in GNNs. Extensive experiments demonstrate that GraphSB significantly outperforms the state-of-the-art methods. More importantly, the proposed Structural Balance can be seamlessly integrated into state-of-the-art methods as a simple plug-and-play module, increasing their accuracy by an average of 3.67\%.

Paper Structure

This paper contains 26 sections, 3 theorems, 39 equations, 8 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Data-level Methods
  4. Algorithm-level Methods
  5. Analysis of Structural imbalance Bias in GNNs
  6. Preliminary
  7. Majority Node Dominance
  8. Minority Feature Assimilation
  9. Conclusion
  10. Methodology
  11. Graph Structure Enhancement
  12. Relation Diffusion Process
  13. Quantity Balancing Strategy
  14. Optimization Objective and Training Strategy
  15. Experiments
  16. ...and 11 more sections

Key Result

Theorem 1

For a path of length $l$ in a class-imbalanced graph with imbalance ratio $\beta$ and degree ratio $\gamma$, the expected path weight satisfies: where $\mathcal{W}_l$ denotes the weight of information propagated along a path of length $l$. $W^{(l)} = \prod_{\ell=1}^l \nabla\phi_\ell \nabla\psi_\ell$ is the cumulative weight matrix product.

Figures (8)

  • Figure 1: Imbalanced node learning on graphs
  • Figure 2: Overview of the proposed GraphSB framework, which incorporates two key steps to enhance the representation of imbalanced data in graph learning.
  • Figure 3: Performance analysis with different imbalance ratios on Cora.
  • Figure 4: Visualization on dataset PubMed.
  • Figure 5: Ablation study: The impact of each module.
  • ...and 3 more figures

Theorems & Definitions (9)

  • Definition 1
  • Theorem 1
  • proof
  • Definition 2
  • Theorem 2
  • proof
  • Definition 3
  • Theorem 3
  • proof