In this paper we explore how to construct a Jensen-Shannon kernel for hypergraphs. We commence by calculating probability distribution over the steady state random walk on a hypergraph. The Shannon entropies required to construct the Jensen-Shannon divergence for pairs of hypergraphs are obtained from steady state probability distributions of the random walk. The Jensen-Shannon divergence between a pair of hypergraphs is the difference between the Shannon entropies of the separate hypergraphs and a composite structure. Our proposed kernel is not restricted to hypergraphs. Experiments on (hyper)graph datasets extracted from bioinformatics and computer vision datasets demonstrate the effectiveness and efficiency of the Jensen-Shannon hypergraph kernel for classification and clustering.


Disjoint Union Shannon Entropy Incidence Matrix Edit Operation Minimum Vertex 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lu Bai
    • 1
  • Edwin R. Hancock
    • 1
  • Peng Ren
    • 2
  1. 1.Department of Computer ScienceUniversity of York, UKHeslingtonUK
  2. 2.College of Information and Control EngineeringChina University of Petroleum (Huadong)China

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