Adaptive page ranking with neural networks
Franco Scarselli, Sweah Liang Yong, Markus Hagenbuchner, Ah Chung Tsoi · 2005
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.