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Directed Laplacian Kernels for Link Analysis

  • Pawel Majewski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

Application of kernel methods to link analysis is presented. Novel kernels based on directed graph Laplacians are proposed and their application as measures of relatedness between nodes in a directed graph is presented. The kernels express relatedness and take into account the global importance of the nodes in a citation graph. Limitations of existing kernels are given with a discussion how they are addressed by directed Laplacian kernels. Links between the kernels and PageRank ranking algorithm are also presented. The proposed kernels are evaluated on a dataset of scientific bibliographic citations.

Keywords

Link Analysis Directed Graph Undirected Graph Transition Probability Matrix Adjacency Matrice 
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 2006

Authors and Affiliations

  • Pawel Majewski
    • 1
  1. 1.Gdańsk University of TechnologyGdańskPoland

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