A Jensen-Shannon Divergence Kernel for Directed Graphs

  • Cheng YeEmail author
  • Richard C. Wilson
  • Edwin R. Hancock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)


Recently, kernel methods have been widely employed to solve machine learning problems such as classification and clustering. Although there are many existing graph kernel methods for comparing patterns represented by undirected graphs, the corresponding methods for directed structures are less developed. In this paper, to fill this gap in the literature we exploit the graph kernels and graph complexity measures, and present an information theoretic kernel method for assessing the similarity between a pair of directed graphs. In particular, we show how the Jensen-Shannon divergence, which is a mutual information measure that gauges the difference between probability distributions, together with the recently developed directed graph von Neumann entropy, can be used to compute the graph kernel. In the experiments, we show that our kernel method provides an efficient tool for classifying directed graphs with different structures and analyzing real-world complex data.


Information theoretic kernel Jensen-Shannon divergence von Neumann entropy 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Cheng Ye
    • 1
    Email author
  • Richard C. Wilson
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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