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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 9

Minority class augmentation and distant connectivity for imbalanced node classification

Han Zhongming
Zhang Shuqun
Liu Yan
Yang Weijie
School of Computer & Artificial Intelligence, Beijing Technology & Business University, Beijing 100048, China
DOI: 10.19734/j.issn.1001-3695.2025.02.0049

Abstract

In real-world, graph data usually presents class-imbalanced distributions. State-of-the-art generative methods propose strategies to synthesize reasonable minority nodes for augmenting imbalanced graphs. However, these methods primarily focus on quantitative compensation. When compensating for the minority class based on its quantity, certain nodes may significantly degrade the performance of other classes. To address this, the paper considered minority class generation from both quantity and topological perspectives and proposed an imbalanced node classification method based on minority class augmentation and distant connectivity. When generating new minority nodes to balance the training data, the imbalance graph is reasonably enhanced by using a node importance-based neighbor sampling approach to identify distant potential nodes of the same class. This alleviated the topological imbalance caused by high ratio of non-self-class neighbors in the node receptive field and poor connectivity with self-class labeled nodes, effectively enhancing the imbalanced graph. The experimental results on three benchmark datasets show that the proposed method outperforms the state-of-the-art methods in the unbalanced node classification task in terms of accuracy, balanced accuracy and F1 value metrics, and the effectiveness and practicality of the method is verified by ablation experiments and application example analyses, among others.

Foundation Support

国家重点研发计划资助项目(2022YFC3302600)
国家自然科学基金资助项目(72171004)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.02.0049
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 9

Publish History

[2025-05-22] Accepted Paper

Cite This Article

韩忠明, 张舒群, 刘燕, 等. 基于少类增强和远距离连通的不平衡节点分类 [J]. 计算机应用研究, 2025, 42 (9). (2025-05-27). https://doi.org/10.19734/j.issn.1001-3695.2025.02.0049. (Han Zhongming, Zhang Shuqun, Liu Yan, et al. Minority class augmentation and distant connectivity for imbalanced node classification [J]. Application Research of Computers, 2025, 42 (9). (2025-05-27). https://doi.org/10.19734/j.issn.1001-3695.2025.02.0049. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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