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Approximating GED Using a Stochastic Generator and Multistart IPFP

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Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11004))

Abstract

The Graph Edit Distance defines the minimal cost of a sequence of elementary operations transforming a graph into another graph. This versatile concept with an intuitive interpretation is a fundamental tool in structural pattern recognition. However, the exact computation of the Graph Edit Distance is \(\mathcal {NP}\)-complete. Iterative algorithms such as the ones based on Franck-Wolfe method provide a good approximation of true edit distance with low execution times. However, underlying cost function to optimize being neither concave nor convex, the accuracy of such algorithms highly depends on the initialization. In this paper, we propose a smart random initializer using promising parts of previously computed solutions.

Work supported by Region Normandie under project RIN AGAC.

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References

  1. Abu-Aisheh, Z., Raveaux, R., Ramel, J.-Y.: A graph database repository and performance evaluation metrics for graph edit distance. In: Liu, C.-L., Luo, B., Kropatsch, W.G., Cheng, J. (eds.) GbRPR 2015. LNCS, vol. 9069, pp. 138–147. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18224-7_14

    Chapter  Google Scholar 

  2. Abu-Aisheh, Z., et al.: Graph edit distance contest: results and future challenges. Pattern Recogn. Lett. 100, 96–103 (2017). https://doi.org/10.1016/j.patrec.2017.10.007

    Article  Google Scholar 

  3. Bougleux, S., Brun, L., Carletti, V., Foggia, P., Gaüzère, B., Vento, M.: Graph edit distance as a quadratic assignment problem. Patt. Recogn. Lett. 87, 38–46 (2017). https://doi.org/10.1016/j.patrec.2016.10.001

    Article  Google Scholar 

  4. Carletti, V., Gaüzère, B., Brun, L., Vento, M.: Approximate graph edit distance computation combining bipartite matching and exact neighborhood substructure distance. In: Liu, C.-L., Luo, B., Kropatsch, W.G., Cheng, J. (eds.) GbRPR 2015. LNCS, vol. 9069, pp. 188–197. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18224-7_19

    Chapter  Google Scholar 

  5. Daller, E., Bougleux, S., Gaüzère, B., Brun, L.: Approximate graph edit distance from several assignments and multiple IPFP. In: International Conference on Pattern Recognition Applications and Methods (2018). https://doi.org/10.5220/0006599901490158

  6. Ferrer, M., Serratosa, F., Riesen, K.: A first step towards exact graph edit distance using bipartite graph matching. In: Liu, C.-L., Luo, B., Kropatsch, W.G., Cheng, J. (eds.) GbRPR 2015. LNCS, vol. 9069, pp. 77–86. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18224-7_8

    Chapter  Google Scholar 

  7. Frank, M., Wolfe, P.: An algorithm for quadratic programming. Nav. Res. Logist. Q. 3(1–2), 95–110 (1956)

    Article  MathSciNet  Google Scholar 

  8. Gaüzère, B., Bougleux, S., Brun, L.: Approximating graph edit distance using GNCCP. In: Robles-Kelly, A., Loog, M., Biggio, B., Escolano, F., Wilson, R. (eds.) S+SSPR 2016. LNCS, vol. 10029, pp. 496–506. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49055-7_44

    Chapter  Google Scholar 

  9. Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image Vis. Comput. 27, 950–959 (2009). https://doi.org/10.1016/j.imavis.2008.04.004

    Article  Google Scholar 

  10. Riesen, K., Bunke, H.: Improving bipartite graph edit distance approximation using various search strategies. Pattern Recogn. 48(4), 1349–1363 (2015). https://doi.org/10.1016/j.patcog.2014.11.002

    Article  MATH  Google Scholar 

  11. Riesen, K., Fischer, A., Bunke, H.: Improved graph edit distance approximation with simulated annealing. In: Foggia, P., Liu, C.-L., Vento, M. (eds.) GbRPR 2017. LNCS, vol. 10310, pp. 222–231. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58961-9_20

    Chapter  Google Scholar 

  12. Sanfeliu, A., Fu, K.S.: A distance measure between attributed relational graphs for pattern recognition. IEEE Trans. Syst. Man Cybern. 13(3), 353–362 (1983). https://doi.org/10.1109/TSMC.1983.6313167

    Article  MATH  Google Scholar 

  13. Wu, Z., et al.: MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. 9, 513–530 (2018). https://doi.org/10.1039/C7SC02664A

    Article  Google Scholar 

  14. Zeng, Z., Tung, A.K.H., Wang, J., Feng, J., Zhou, L.: Comparing stars: on approximating graph edit distance. Proc. VLDB Endow. 2(1), 25–36 (2009). https://doi.org/10.14778/1687627.1687631

    Article  Google Scholar 

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Authors and Affiliations

  1. Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYC, Caen, France

    Nicolas Boria, Sébastien Bougleux & Luc Brun

Authors
  1. Nicolas Boria
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  2. Sébastien Bougleux
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  3. Luc Brun
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Corresponding author

Correspondence to Nicolas Boria .

Editor information

Editors and Affiliations

  1. Beihang University, Beijing, China

    Xiao Bai

  2. University of York, York, United Kingdom

    Edwin R. Hancock

  3. IBM Research – Thomas J. Watson Research, Yorktown Heights, New York, USA

    Tin Kam Ho

  4. University of York, Heslington, York, United Kingdom

    Richard C. Wilson

  5. University of Cagliari, Cagliari, Italy

    Battista Biggio

  6. Data 61 - CSIRO, Canberra, Aust Capital Terr, Australia

    Antonio Robles-Kelly

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Boria, N., Bougleux, S., Brun, L. (2018). Approximating GED Using a Stochastic Generator and Multistart IPFP. In: Bai, X., Hancock, E., Ho, T., Wilson, R., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2018. Lecture Notes in Computer Science(), vol 11004. Springer, Cham. https://doi.org/10.1007/978-3-319-97785-0_44

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  • DOI: https://doi.org/10.1007/978-3-319-97785-0_44

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  • Print ISBN: 978-3-319-97784-3

  • Online ISBN: 978-3-319-97785-0

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