SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks

Buffelli, Davide; Liò, Pietro; Vandin, Fabio

SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks

Davide Buffelli;Pietro Liò;Fabio Vandin

2022

Scheda breve

Scheda completa

Scheda completa (DC)

	Anno
	
				2022
			
	Titolo del Libro
	
				Proceedings of 36th Conference on Neural Information Processing Systems (NeurIPS 2022)
			
	Titolo convegno
	
				36th Conference on Neural Information Processing Systems (NeurIPS 2022)
			
	Codice WOS
	
				WOS:001213927508035
			
	Codice Scopus
	
				2-s2.0-85140194345
			
	Codice OpenAlex
	
				W4286233743
			
	Codice ISBN
	
				9781713871088
			
	Appare nelle tipologie:
	
				04.01 - Contributo in atti di convegno

File in questo prodotto:

File	Dimensione	Formato
NeurIPS-2022-sizeshiftreg-a-regularization-method-for-improving-size-generalization-in-graph-neural-networks-Paper-Conference (1).pdf accesso aperto Tipologia: Published (Publisher's Version of Record) Licenza: Accesso libero Dimensione 1.29 MB Formato Adobe PDF Visualizza/Apri	1.29 MB	Adobe PDF	Visualizza/Apri
NeurIPS-2022-sizeshiftreg-a-regularization-method-for-improving-size-generalization-in-graph-neural-networks-Supplemental-Conference.pdf accesso aperto Descrizione: Appendix for the Paper: “SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks” Tipologia: Altro materiale allegato Licenza: Accesso libero Dimensione 235.07 kB Formato Adobe PDF Visualizza/Apri	235.07 kB	Adobe PDF	Visualizza/Apri