Eigenvector Distance-Modulated Graph Neural Network: Spectral Weighting for Enhanced Node Classification

Graph Neural Networks (GNNs) face significant challenges in node classification across diverse graph structures. Traditional message passing mechanisms often fail to adaptively weight node relationships, thereby limiting performance in both homophilic and heterophilic graph settings. We propose the...

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Detalhes bibliográficos

Principais autores:	Ahmed Begga, Francisco Escolano, Miguel Ángel Lozano
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2025-09-01
coleção:	Mathematics
Assuntos:	graph neural networks spectral graph theory eigenvector distance node classification edge weighting graph representation learning
Acesso em linha:	https://www.mdpi.com/2227-7390/13/17/2895
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