Graph Neural Networks for Ranking Web Pages

An artificial neural network model, capable of processing general types of graph structured data, has recently been proposed. This paper applies the new model to the computation of customised page ranks problem in the World Wide Web. The class of customised page ranks that can be implemented in this...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Scarselli, Franco, Yong, Sweah Liang, Gori, Marco, Hagenbuchner, Markus, Tsoi, Ah Chung, Maggini, Marco
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Algorithm design and analysis Artificial neural networks Australia Council Computational modeling Computer systems organization > Architectures > Other architectures > Neural networks Computing methodologies > Machine learning Computing methodologies > Machine learning > Machine learning approaches > Neural networks Damping Information systems > Information retrieval Information systems > Information retrieval > Evaluation of retrieval results > Relevance assessment Information systems > Information storage systems Mathematics of computing > Discrete mathematics > Graph theory > Graph algorithms Neural networks Search engines Sorting Web pages Web sites
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	An artificial neural network model, capable of processing general types of graph structured data, has recently been proposed. This paper applies the new model to the computation of customised page ranks problem in the World Wide Web. The class of customised page ranks that can be implemented in this way is very general and easy because the neural network model is learned by examples. Some preliminary experimental findings show that the model generalizes well over unseen web pages, and hence, may be suitable for the task of page rank computation on a large web graph.
DOI:	10.1109/WI.2005.67