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Random Walk Classifier Framework on Graph

  • Xiaohua Xu
  • Lin Lu
  • Ping He
  • Zhoujin Pan
  • Ling Chen
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)

Abstract

A novel semi-supervised classification framework is proposed based on the label propagation using random walks on graph. To characterize this model, two classifiers, namely the lazy and single-step random walk classifiers are specifically derived. Sufficient experiments and comparison prove their universal adaptability and good performance.

Keywords

random walk classification semi-supervised learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiaohua Xu
    • 1
  • Lin Lu
    • 1
  • Ping He
    • 1
  • Zhoujin Pan
    • 1
  • Ling Chen
    • 1
  1. 1.Department of Computer ScienceYangzhou UniversityYangzhouChina

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