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Parallel Vector Computing Technique for Discovering Communities on the Very Large Scale Web Graph

  • Kikuko Kawase
  • Minoru Kawahara
  • Takeshi Iwashita
  • Hiroyuki Kawano
  • Masanori Kawazawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2737)

Abstract

The study of the authoritative pages and community discovery from an enormous Web contents has attracted many researchers. One of the link-based analysis, the HITS algorithm, calculates authority scores as the eigenvector of a adjacency matrix created from the Web graph. Although it was considered impossible to compute the eigenvector of a very large scale of Web graph using previous techniques, due to this calculation requires enormous memory space. We make it possible using data compression and parallel computation.

Keywords

Adjacency Matrix Main Memory Average Link Link Structure Vector Calculation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Kikuko Kawase
    • 1
  • Minoru Kawahara
    • 2
  • Takeshi Iwashita
    • 2
  • Hiroyuki Kawano
    • 1
  • Masanori Kawazawa
    • 2
  1. 1.Department of Systems ScienceGraduate School of Informatics, Kyoto University 
  2. 2.Academic Center for Computing and Media StudiesKyoto University 

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