In this paper, we present a new neural network model, called graph neural network model, which is a generalization of two existing approaches, viz., the graph focused approach, and the node focused approach. The graph focused approach considers the mapping from a graph structure to a real vector, in which the mapping is independent of the particular node involved; while the node focused approach considers the mapping from a graph structure to a real vector, in which the mapping depends on the properties of the node involved. It is shown that the graph neural network model maintains some of the characteristics of the graph focused models and the node focused models respectively. A supervised learning algorithm is derived to estimate the parameters of the graph neural network model. Some experimental results are shown to validate the proposed learning algorithm, and demonstrate the generalization capability of the proposed model

Scarselli, F., Ah Chung, T., Gori, M., Markus, H. (2004). Graphical-based learning environments for pattern recognition. In STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION, PROCEEDINGS (pp.42-56). Berlin Heidelberg : Springer-Verlag [10.1007/978-3-540-27868-9_4].

Graphical-based learning environments for pattern recognition

SCARSELLI, FRANCO;GORI, MARCO;
2004-01-01

Abstract

In this paper, we present a new neural network model, called graph neural network model, which is a generalization of two existing approaches, viz., the graph focused approach, and the node focused approach. The graph focused approach considers the mapping from a graph structure to a real vector, in which the mapping is independent of the particular node involved; while the node focused approach considers the mapping from a graph structure to a real vector, in which the mapping depends on the properties of the node involved. It is shown that the graph neural network model maintains some of the characteristics of the graph focused models and the node focused models respectively. A supervised learning algorithm is derived to estimate the parameters of the graph neural network model. Some experimental results are shown to validate the proposed learning algorithm, and demonstrate the generalization capability of the proposed model
2004
9783540225706
9783540278689
Scarselli, F., Ah Chung, T., Gori, M., Markus, H. (2004). Graphical-based learning environments for pattern recognition. In STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION, PROCEEDINGS (pp.42-56). Berlin Heidelberg : Springer-Verlag [10.1007/978-3-540-27868-9_4].
File in questo prodotto:
File Dimensione Formato  
SSPR2004.pdf

non disponibili

Tipologia: PDF editoriale
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 234.91 kB
Formato Adobe PDF
234.91 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/37347
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo