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    This study introduces a novel knowledge distillation (KD) framework for graph neural networks (GNNs) that effectively transfers knowledge across graph types. The proposed graph framelet method enables student models to achieve teacher-level accuracy while significantly improving inference speed.

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    Area of Science:

    • Graph Representation Learning
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Knowledge distillation (KD) accelerates complex model inference by transferring knowledge to simpler student models.
    • Existing KD methods for graph neural networks (GNNs) often overlook graph-specific knowledge, using generic approximators like MLPs.
    • This limits their ability to effectively learn from teacher GNNs, especially on diverse graph structures.

    Purpose of the Study:

    • To develop a KD framework for GNNs that leverages multiscaled graph knowledge.
    • To enable student models to adapt to both homophilic and heterophilic graphs.
    • To address the oversquashing issue in GNNs and improve inference efficiency without sacrificing accuracy.

    Main Methods:

    • Proposed a KD framework based on multiscaled GNNs, termed graph framelet.
    • Utilized graph framelet decomposition to extract and transfer multiscaled graph knowledge.
    • Employed graph surgery techniques to alleviate the oversquashing problem.
    • Analyzed knowledge transfer through algebraic and geometric perspectives.

    Main Results:

    • The proposed graph framelet method enables student models to adapt to both homophilic and heterophilic graphs.
    • Demonstrated the potential to alleviate the oversquashing issue through graph surgery.
    • Achieved learning accuracy identical to or surpassing the teacher model.
    • Maintained high inference speed comparable to simpler student models.

    Conclusions:

    • The graph framelet framework effectively transfers multiscaled graph knowledge, enhancing student GNN performance.
    • This approach successfully adapts to diverse graph properties and mitigates common GNN challenges.
    • The method offers a promising direction for efficient and accurate graph representation learning.