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High-order Graph Neural Networks (HO-GNNs) have been developed to infer consistent latent spaces in the heterophilic regime, where the label distribution is not correlated with the graph structure. However, most of the existing HO-GNNs are hop-based,
Externí odkaz:
http://arxiv.org/abs/2306.16976
Publikováno v:
Proceedings of the First Learning on Graphs Conference (LoG 2022), PMLR 198, Virtual Event, December, 2022
Graph Neural Networks (GNNs) have been shown to achieve competitive results to tackle graph-related tasks, such as node and graph classification, link prediction and node and graph clustering in a variety of domains. Most GNNs use a message passing f
Externí odkaz:
http://arxiv.org/abs/2206.07369
Publikováno v:
In Neural Networks January 2025 181
Akademický článek
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Autor:
Begga A; Department of Computer Science and Artificial Intelligence, Alicante, Spain. Electronic address: ahmed.begga@ua.es., Escolano F; Department of Computer Science and Artificial Intelligence, Alicante, Spain. Electronic address: sco@ua.es., Lozano MÁ; Department of Computer Science and Artificial Intelligence, Alicante, Spain.
Publikováno v:
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2024 Oct 26; Vol. 181, pp. 106830. Date of Electronic Publication: 2024 Oct 26.