Attributed graph similarity from the quantum jensen-shannon divergence

  • Authors:

  • Luca Rossi;Andrea Torsello;Edwin R. Hancock

  • Affiliations:

  • Department of Environmental Science, Informatics and Statistics, Ca' Foscari University of Venice, Italy;Department of Environmental Science, Informatics and Statistics, Ca' Foscari University of Venice, Italy;Department of Computer Science, University of York, UK

  • Venue:
  • SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
  • Year:
  • 2013

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Abstract

One of the most fundamental problem that we face in the graph domain is that of establishing the similarity, or alternatively the distance, between graphs. In this paper, we address the problem of measuring the similarity between attributed graphs. In particular, we propose a novel way to measure the similarity through the evolution of a continuous-time quantum walk. Given a pair of graphs, we create a derived structure whose degree of symmetry is maximum when the original graphs are isomorphic, and where a subset of the edges is labeled with the similarity between the respective nodes. With this compositional structure to hand, we compute the density operators of the quantum systems representing the evolution of two suitably defined quantum walks. We define the similarity between the two original graphs as the quantum Jensen-Shannon divergence between these two density operators, and then we show how to build a novel kernel on attributed graphs based on the proposed similarity measure. We perform an extensive experimental evaluation both on synthetic and real-world data, which shows the effectiveness the proposed approach.