An End-To-End Hyperbolic Deep Graph Convolutional Neural Network Framework
Yuchen Zhou,
Hongtao Huo,
Zhiwen Hou
et al.
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 hie… Show more
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