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It is crucial to address category imbalance in real-world data on node classification in a graph. This paper introduces the GraphDHV model, a dual encoder that integrates node attributes and topological information. GraphDHV improves inter-class separability and intra-class compactness by employing degree logical edge removal and synthesizing enhanced attribute features with original topology. We compare GraphDHV with ten state-of-the-art baseline methods on six public benchmark datasets. Our experimental results demonstrate that GraphDHV significantly outperforms the baseline methods, with an improvement of the F1 score.

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GraphDHV: Graph Neural Network with Dual Hybrid View on Imbalanced Node Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Longqing Du,
  • Guangquan Lu,
  • Yadan Han,
  • Zhiping Luo,
  • Guoqiu Wen,
  • Liang Zhang,
  • Wanxin Chen,
  • Shichao Zhang

摘要

It is crucial to address category imbalance in real-world data on node classification in a graph. This paper introduces the GraphDHV model, a dual encoder that integrates node attributes and topological information. GraphDHV improves inter-class separability and intra-class compactness by employing degree logical edge removal and synthesizing enhanced attribute features with original topology. We compare GraphDHV with ten state-of-the-art baseline methods on six public benchmark datasets. Our experimental results demonstrate that GraphDHV significantly outperforms the baseline methods, with an improvement of the F1 score.