A Fast Jensen-Shannon Subgraph Kernel

  • Lu Bai
  • Edwin R. Hancock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)

Abstract

In this paper we present a fast subgraph kernel based on Jensen-Shannon divergence and depth-based representations. For graphs with n vertices and m edges, the worst-case time complexity for our kernel is O(n 3 + mn), in contrast to O(n 6) for the classic graph kernel. Key to this efficiency is that we manage to compute the Jensen-Shannon divergence involved in our kernel with O(n 2) operations. This computational strategy enables our subgraph kernel to easily scale up to graphs of reasonably large sizes and thus overcome the size limits arising in state of the art graph kernels. Experiments on standard bioinformatics graph datasets together with graph datasets extracted from images demonstrate the effectiveness and efficiency of our subgraph kernel.

Keywords

Shannon Entropy Short Path Length Machine Learn Research Graph Kernel Algebraic Graph Theory 
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 2013

Authors and Affiliations

  • Lu Bai
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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