Yuchen Zhou1, Hongtao Huo1, Zhiwen Hou1, Lingbin Bu1, Yifan Wang1, Jingyi Mao1, Xiaojun Lv2, Fanliang Bu1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 537-563, 2024, DOI:10.32604/cmes.2023.044895
Abstract Graph Convolutional Neural Networks (GCNs) have been widely used in various fields due to their powerful capabilities in processing graph-structured data. However, GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions, resulting in substantial distortions. Moreover, most of the existing GCN models are shallow structures, which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures. To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations, we propose… More >
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