A neural network approach to web graph processing

  • Authors:
  • Ah Chung Tsoi;Franco Scarselli;Marco Gori;Markus Hagenbuchner;Sweah Liang Yong

  • Affiliations:
  • Australian Research Council, Canberra, Australia;University of Siena, Siena, Italy;University of Siena, Siena, Italy;Faculty of Informatics, University of Wollongong, Wollongong, Australia;Faculty of Informatics, University of Wollongong, Wollongong, Australia

  • Venue:
  • APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
  • Year:
  • 2005

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Abstract

In this paper, we will provide an overview of some of the more recent developments in web graph processing using the classic Google page rank equation as popularized by Brins and Page [1], and its modifications, to handle page rank and personalized page rank determinations. It is shown that one may progressively modify the linear matrix stochastic equation underlying the Google page rank determinations [1] to one which may contain neural network formulations. Furthermore the capability of these modifications in determining personalized page ranks is demonstrated through a number of examples based on the web repository WT10G.