• DocumentCode
    595357
  • Title

    Hash-based structural similarity for semi-supervised Learning on attribute graphs

  • Author

    Hido, Shohei ; Kashima, Hideyuki

  • Author_Institution
    IBM Res., Tokyo, Japan
  • fYear
    2012
  • fDate
    11-15 Nov. 2012
  • Firstpage
    3009
  • Lastpage
    3012
  • Abstract
    We present an efficient method to compute similarity between graph nodes by comparing their neighborhood structures rather than proximity. The key is to use a hash for avoiding expensive subgraph comparison. Experiments show that the proposed algorithm performs well in semi-supervised node classification.
  • Keywords
    graph theory; learning (artificial intelligence); pattern matching; attribute graphs; hash-based structural similarity; semisupervised learning; semisupervised node classification; Arrays; Educational institutions; Kernel; Labeling; Pattern recognition; Proteins; Time measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition (ICPR), 2012 21st International Conference on
  • Conference_Location
    Tsukuba
  • ISSN
    1051-4651
  • Print_ISBN
    978-1-4673-2216-4
  • Type

    conf

  • Filename
    6460798
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=595357