paper improves GCN generalization by minimizing the ∗Corresponding feature matrix obtained by stacking multiple GCN layers While the intrinsic geom-
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[PDF] Simple and Deep Graph Convolutional Networks - Proceedings of
three variants of Geom-GCN (Pei et al , 2020) as they are the state-of-the-art models on these datasets Table 5 reports the mean classification accuracy of each
[PDF] Beyond Homophily in Graph Neural Networks - NeurIPS
Geom-GCN [26] precomputes unsupervised node embeddings and uses neighborhoods defined by geometric relationships in the resulting latent space to define
[PDF] Beyond Homophily in Graph Neural Networks: Current - Jiong Zhu
(2020) ”Geom-GCN: Geometric Graph Convolutional Networks” ICLR 0 20 40 60 80 GCN
[PDF] Fisher-Bures Adversary Graph Convolutional Networks
paper improves GCN generalization by minimizing the ∗Corresponding feature matrix obtained by stacking multiple GCN layers While the intrinsic geom-
Node Similarity Preserving Graph Convolutional - ResearchGate
Geom-GCN [27]: Geom-GCN explores to capture long-range dependencies in disassortative graphs It uses the geometric rela- tionships defined in the latent
Implicit Graph Neural Networks - NIPS Proceedings - NeurIPS
However, Geom-GCN (Pei et al , 2020) also belongs to convolutional-based GNNs, which struggle to capture very long range dependency due to the finite
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