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Learning Aligned Vertex Convolutional Networks for Graph Classification.

Lixin Cui, Lu Bai, Xiao Bai

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    Aligned Vertex Convolutional Networks (AVCNs) tackle graph convolutional network oversmoothing. Our novel AVCN models learn multiscale vertex features, enhancing graph classification accuracy by avoiding redundant information propagation.

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    Area of Science:

    • Graph Neural Networks
    • Machine Learning
    • Computer Science

    Background:

    • Graph Convolutional Networks (GCNs) excel at graph data analysis.
    • A key limitation of GCNs is oversmoothing, where deep layers yield indistinguishable vertex features.
    • This hinders performance in complex graph classification tasks.

    Purpose of the Study:

    • To introduce Aligned Vertex Convolutional Network (AVCN) models.
    • To address the oversmoothing problem in GCNs.
    • To improve graph classification by learning multiscale vertex features.

    Main Methods:

    • Developed a transitive vertex alignment algorithm to convert graphs into fixed-size grids.
    • Introduced a novel aligned vertex convolution operation for multiscale feature aggregation.
    • Proposed two AVCN architectures for hierarchical feature extraction.

    Main Results:

    • AVCN models effectively learn multiscale vertex representations.
    • The proposed method mitigates the oversmoothing issue by preventing redundant information propagation.
    • Experimental results on benchmark datasets confirm the model's effectiveness.

    Conclusions:

    • AVCNs offer a promising solution to the oversmoothing problem in GCNs.
    • The proposed aligned vertex convolution operation enables robust multiscale feature learning.
    • AVCNs demonstrate superior performance in graph classification tasks.