Advertisement

Approximate Graph Edit Distance Computation Combining Bipartite Matching and Exact Neighborhood Substructure Distance

  • Vincenzo CarlettiEmail author
  • Benoit Gaüzère
  • Luc Brun
  • Mario Vento
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9069)

Abstract

Graph edit distance corresponds to a flexible graph dissimilarity measure. Unfortunately, its computation requires an exponential complexity according to the number of nodes of both graphs being compared. Some heuristics based on bipartite assignment algorithms have been proposed in order to approximate the graph edit distance. However, these heuristics lack of accuracy since they are based either on small patterns providing a too local information or walks whose tottering induce some bias in the edit distance calculus. In this work, we propose to extend previous heuristics by considering both less local and more accurate patterns using subgraphs defined around each node.

Keywords

Edit Distance Direct Neighbourhood Heuristic Function Edit Operation Optimal Assignment 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in Pattern Recognition. International Journal of Pattern Recognition and Artificial Intelligence 18(3), 265–298 (2004)CrossRefGoogle Scholar
  2. 2.
    Foggia, P., Percannella, G., Vento, M.: Graph Matching and Learning in Pattern Recognition on the last ten years. Journal of Pattern Recognition and Artificial Intelligence 28(1) (2014)Google Scholar
  3. 3.
    Gaüzère, B., Bougleux, S., Riesen, K., Brun, L.: Approximate Graph Edit Distance Guided by Bipartite Matching of Bags of Walks. In: Fränti, P., Brown, G., Loog, M., Escolano, F., Pelillo, M. (eds.) S+SSPR 2014. LNCS, vol. 8621, pp. 73–82. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  4. 4.
    Gibert, J., Valveny, E., Bunke, H.: Graph embedding in vector spaces by node attribute statistics. Pattern Recognition 45(9), 3072–3083 (2012)CrossRefGoogle Scholar
  5. 5.
    Luo, B., Wilson, R.C., Hancock, E.R.: Spectral embedding of graphs. Pattern Recognition 36(10), 2213–2230 (2003)CrossRefzbMATHGoogle Scholar
  6. 6.
    Munkres, J.: Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics 5(1), 32–38 (1957)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Neuhaus, M., Riesen, K., Bunke, H.: Fast suboptimal algorithms for the computation of graph edit distance. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 163–172. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Ramon, J., Gärtner, T.: Expressivity versus efficiency of graph kernels. In: First International Workshop on Mining Graphs, Trees and Sequences, pp. 65–74 (2003)Google Scholar
  9. 9.
    Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image and Vision Computing 27, 950–959 (2009)CrossRefGoogle Scholar
  10. 10.
    Riesen, K., Emmenegger, S., Bunke, H.: A novel software toolkit for graph edit distance computation. In: Kropatsch, W.G., Artner, N.M., Haxhimusa, Y., Jiang, X. (eds.) GbRPR 2013. LNCS, vol. 7877, pp. 142–151. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Riesen, K., Fischer, A., Bunke, H.: Improving Approximate Graph Edit Distance Using Genetic Algorithms. In: Fränti, P., Brown, G., Loog, M., Escolano, F., Pelillo, M. (eds.) S+SSPR 2014. LNCS, vol. 8621, pp. 63–72. Springer, Heidelberg (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Vincenzo Carletti
    • 1
    Email author
  • Benoit Gaüzère
    • 1
  • Luc Brun
    • 2
  • Mario Vento
    • 1
  1. 1.DIEM, Department of Information Engineering, Electrical Engineering and Applied MathematicsUniversity of SalernoFiscianoItaly
  2. 2.GREYC CNRS UMR 6072, ENSICAENCaenFrance

Personalised recommendations