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Updated: May 24, 2025

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    This study introduces a novel perturbation technique to combat overfitting in graph neural networks (GNNs) caused by sparse node features. The method enhances node classification performance by improving training variability and reducing prediction variance.

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    Area of Science:

    • Machine Learning
    • Graph Neural Networks

    Background:

    • Graph neural networks (GNNs) are widely used in semi-supervised learning, with research focusing on effective graph filters and aggregation methods.
    • Challenges arise from sparse training nodes and features (e.g., bag-of-words), leading to overfitting in projection matrices.

    Purpose of the Study:

    • To address the overfitting issue in GNNs caused by sparse node features.
    • To propose an innovative perturbation technique to enhance GNN performance.

    Main Methods:

    • Introducing a novel perturbation technique that modifies initial features and the hyperplane.
    • Increasing training variability to update all dimensions and reduce prediction variance.

    Main Results:

    • The proposed method significantly enhances node classification performance on real-world datasets.
    • Achieved improvements of up to 46.5% in GNN algorithms.

    Conclusions:

    • This approach is the first to tackle GNN overfitting stemming from sparse node features.
    • The perturbation technique effectively mitigates overfitting and boosts classification accuracy.