Adaptive page ranking with neural networks

Authors:
Franco Scarselli;Sweah Liang Yong;Markus Hagenbuchner;Ah Chung Tsoi
Affiliations:
University of Siena, Siena, Italy;University of Wollongong, Wollongong, Australia;University of Wollongong, Wollongong, Australia;Australian Research Council, Canberra, Australia
Venue:
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Year:
2005

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Abstract

Recent developments in the area of neural networks provided new models which are capable of processing general types of graph structures. Neural networks are well-known for their generalization capabilities. This paper explores the idea of applying a novel neural network model to a web graph to compute an adaptive ranking of pages. Some early experimental results indicate that the new neural network models generalize exceptionally well when trained on a relatively small number of pages.