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Hyperbolic graph convolutional networks (HGCNs) have demonstrated representational capabilities of modeling hierarchical-structured graphs. However, as in general GCNs, over-smoothing may occur as the number of model layers increases, limiting the re
Externí odkaz:
http://arxiv.org/abs/2412.03825
Hyperbolic graph convolutional networks (GCNs) demonstrate powerful representation ability to model graphs with hierarchical structure. Existing hyperbolic GCNs resort to tangent spaces to realize graph convolution on hyperbolic manifolds, which is i
Externí odkaz:
http://arxiv.org/abs/2104.06942
Publikováno v:
In Pattern Recognition April 2022 124
