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    This study introduces a novel non-local aggregation framework for graph neural networks (GNNs), enhancing performance on disassortative graphs. The proposed attention-guided sorting method significantly improves both efficiency and accuracy in graph analysis tasks.

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    Area of Science:

    • Graph Neural Networks (GNNs)
    • Machine Learning
    • Network Analysis

    Background:

    • Modern GNNs excel on assortative graphs using local aggregation.
    • Disassortative graphs often necessitate non-local aggregation for optimal performance.
    • Local aggregation can be detrimental to certain disassortative graph structures.

    Purpose of the Study:

    • To develop a novel non-local aggregation framework for GNNs.
    • To address the limitations of local aggregation in disassortative graph learning.
    • To improve the performance and efficiency of GNNs on challenging graph types.

    Main Methods:

    • Proposed a simple yet effective non-local aggregation framework.
    • Incorporated efficient attention-guided sorting mechanism for GNNs.
    • Developed and evaluated various non-local GNN models.

    Main Results:

    • Non-local GNNs significantly outperformed state-of-the-art methods on seven benchmark disassortative graph datasets.
    • Demonstrated superior model performance and efficiency compared to existing approaches.
    • Validated the effectiveness of attention-guided sorting in non-local aggregation.

    Conclusions:

    • The proposed non-local aggregation framework is highly effective for disassortative graphs.
    • Attention-guided sorting enhances GNN capabilities for complex graph structures.
    • This work advances GNN applications in domains characterized by disassortative networks.