Random projection forest initialization for graph convolutional networks.
Deep learning
Graph convolutional network (GCN)
Graph neural network (GNN)
Random Projection Forest Initialization for Graph Convolutional Networks
Random projection forests
Journal
MethodsX
ISSN: 2215-0161
Titre abrégé: MethodsX
Pays: Netherlands
ID NLM: 101639829
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
07
11
2022
accepted:
03
08
2023
medline:
21
8
2023
pubmed:
21
8
2023
entrez:
21
8
2023
Statut:
epublish
Résumé
Graph convolutional networks (GCNs) were a great step towards extending deep learning to graphs. GCN uses the graph
Identifiants
pubmed: 37601292
doi: 10.1016/j.mex.2023.102315
pii: S2215-0161(23)00312-6
pmc: PMC10433121
doi:
Types de publication
Journal Article
Langues
eng
Pagination
102315Informations de copyright
© 2023 The Author(s).
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Références
Nonlinear Biomed Phys. 2007 Jul 05;1(1):3
pubmed: 17908336
IEEE Trans Neural Netw Learn Syst. 2021 Jan;32(1):4-24
pubmed: 32217482
IEEE Trans Pattern Anal Mach Intell. 2023 Jun;45(6):6923-6939
pubmed: 33872143