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Shared Growth of Graph Neural Networks via Prompted Free-Direction Knowledge Distillation.

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    This study introduces FreeKD, a novel framework for knowledge distillation in graph neural networks (GNNs). FreeKD enables collaborative learning between shallow GNNs, overcoming challenges associated with training deep GNNs for effective knowledge transfer.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Graph Neural Networks

    Background:

    • Knowledge distillation (KD) typically enhances graph neural network (GNN) performance by transferring knowledge from deep to shallow models.
    • Training deep GNNs is hindered by over-parameterization and over-smoothing, compromising effective knowledge transfer.
    • Existing KD methods require a well-optimized deep teacher GNN, which is often difficult to achieve.

    Purpose of the Study:

    • To propose a novel Free-direction Knowledge Distillation (FreeKD) framework for GNNs that eliminates the need for a deep teacher GNN.
    • To enable collaborative learning and knowledge exchange between multiple shallow GNNs using reinforcement learning.
    • To enhance knowledge transfer by incorporating dynamic, free-direction strategies and diverse graph augmentations.

    Main Methods:

    • Developed FreeKD, a reinforcement learning-based framework for collaborative learning between two shallow GNNs.
    • Introduced a hierarchical reinforcement learning strategy with node-level and structure-level actions for dynamic knowledge transfer.
    • Proposed FreeKD-Prompt for learning undistorted, diverse augmentations via prompt learning to exchange varied knowledge.
    • Extended the framework to FreeKD++ and FreeKD-Prompt++ for knowledge transfer among multiple shallow GNNs.

    Main Results:

    • FreeKD and its variants significantly outperform baseline GNNs across five benchmark datasets.
    • The proposed methods demonstrate efficacy across various GNN architectures.
    • FreeKD achieves comparable or superior performance to traditional KD methods using deep teacher GNNs.

    Conclusions:

    • FreeKD offers an effective alternative to traditional KD by enabling collaborative learning between shallow GNNs.
    • The framework successfully addresses the limitations of training deep GNNs for knowledge distillation.
    • FreeKD provides a flexible and powerful approach for enhancing GNN performance through novel knowledge transfer mechanisms.