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Multichannel Adaptive Data Mixture Augmentation for Graph Neural Networks

Author

Listed:
  • Zhonglin Ye

    (Qinghai Normal University, China)

  • Lin Zhou

    (Qinghai Normal University, China)

  • Mingyuan Li

    (Qinghai Normal University, China)

  • Wei Zhang

    (Qinghai Normal University, China)

  • Zhen Liu

    (Nagasaki Institute of Applied Science, Japan)

  • Haixing Zhao

    (Qinghai Normal University, China)

Abstract

Graph neural networks (GNNs) have demonstrated significant potential in analyzing complex graph-structured data. However, conventional GNNs encounter challenges in effectively incorporating global and local features. Therefore, this paper introduces a novel approach for GNN called multichannel adaptive data mixture augmentation (MAME-GNN). It enhances a GNN by adopting a multi-channel architecture and interactive learning to effectively capture and coordinate the interrelationships between local and global graph structures. Additionally, this paper introduces the polynomial–Gaussian mixture graph interpolation method to address the problem of single and sparse graph data, which generates diverse and nonlinear transformed samples, improving the model's generalization ability. The proposed MAME-GNN is validated through extensive experiments on publicly available datasets, showcasing its effectiveness. Compared to existing GNN models, the MAME-GNN exhibits superior performance, significantly enhancing the model's robustness and generalization ability.

Suggested Citation

  • Zhonglin Ye & Lin Zhou & Mingyuan Li & Wei Zhang & Zhen Liu & Haixing Zhao, 2024. "Multichannel Adaptive Data Mixture Augmentation for Graph Neural Networks," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 20(1), pages 1-14, January.
  • Handle: RePEc:igg:jdwm00:v:20:y:2024:i:1:p:1-14
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    References listed on IDEAS

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