Adaptive page ranking with neural networks

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.