Graphneural networks (GNN) are a type of deep learning that is designed to handlenon-Euclidean issues using graph-structured data that are difficult to solve with traditional deep learning techniques . The majority of GNNs were createdusing a variety of processes, including random walk, PageRank, graphconvolution, and heat diffusion, making direct comparisons impossible . Previous studies have primarily focused on classifying current models into distinct categories, with little investigation of their internal relationships . Thisresearch proposes a unified theoretical framework and a novel perspective that can methodologically integrate existing GNN into our framework . Further investigation reveals a strong relationship between the spatial, spectral, and subgroups of these domains .

Author(s) : Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, Chang-Tien Lu

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Keywords : framework - domains - investigation - spatial - graph -

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