Skip to main content

Solving the Graph Edit Distance Problem with Variable Partitioning Local Search

  • Conference paper
  • First Online:
Graph-Based Representations in Pattern Recognition (GbRPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11510))

Abstract

In the world of graph matching, the Graph Edit Distance (GED) problem is a well-known distance measure between graphs. It has been proven to be a \(\mathcal {NP}\)-hard minimization problem. This paper presents an adapted version of Variable Partitioning Local Search (VPLS) matheuristic for solving the GED problem. The main idea in VPLS is to perform local searches in the solution space of a Mixed Integer Linear Program (MILP). A local search is done in a small neighborhood defined based on a set of special variables. Those special variables are selected based on a procedure that extracts useful characteristics from the instance at hand. This actually ensures that the neighborhood contains high quality solutions. Finally, the experimentation results have shown that VPLS has outperformed existing heuristics in terms of solution quality on CMU-HOUSE database.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bougleux, S., Brun, L., Carletti, V., Foggia, P., Gaüzère, B., Vento, M.: Graph edit distance as a quadratic assignment problem. Pattern Recogn. Lett. 87, 38–46 (2017)

    Article  Google Scholar 

  2. Bunke, H.: On a relation between graph edit distance and maximum common subgraph. Pattern Recogn. Lett. 18(8), 689–694 (1997)

    Article  MathSciNet  Google Scholar 

  3. Bunke, H.: Error correcting graph matching: on the influence of the underlying cost function. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 917–922 (1999)

    Article  Google Scholar 

  4. Bunke, H., Allermann, G.: Inexact graph matching for structural pattern recognition. Pattern Recogn. Lett. 1(4), 245–253 (1983)

    Article  Google Scholar 

  5. Cormen, T.H.: Section 24.3: Dijkstra’s algorithm. In: Introduction to Algorithms, pp. 595–601 (2001)

    Google Scholar 

  6. Darwiche, M., Conte, D., Raveaux, R., T’Kindt, V.: Graph edit distance: accuracy of local branching from an application point of view. Pattern Recogn. Lett. (2018). https://doi.org/10.1016/j.patrec.2018.03.033. http://www.sciencedirect.com/science/article/pii/S0167865518301119

  7. Darwiche, M., Conte, D., Raveaux, R., T’Kindt, V.: A local branching heuristic for solving a graph edit distance problem. Comput. Oper. Res. (2018). https://doi.org/10.1016/j.cor.2018.02.002. http://www.sciencedirect.com/science/article/pii/S0305054818300339

  8. Darwiche, M., Raveaux, R., Conte, D., T’Kindt, V.: Graph edit distance in the exact context. In: Bai, X., Hancock, E.R., Ho, T.K., Wilson, R.C., Biggio, B., Robles-Kelly, A. (eds.) S+SSPR 2018. LNCS, vol. 11004, pp. 304–314. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-97785-0_29

    Chapter  Google Scholar 

  9. Croce, F.D., Grosso, A., Salassa, F.: Matheuristics: embedding MILP solvers into heuristic algorithms for combinatorial optimization problems. In: Siarry, P. (ed.) The Oxford Handbook of Innovation, Chap. 3. NOVA Publisher (2013)

    Google Scholar 

  10. Ferrer, M., Serratosa, F., Riesen, K.: Improving bipartite graph matching by assessing the assignment confidence. Pattern Recogn. Lett. 65, 29–36 (2015)

    Article  Google Scholar 

  11. Justice, D., Hero, A.: A binary linear programming formulation of the graph edit distance. IEEE Trans. Pattern Anal. Mach. Intell. 28(8), 1200–1214 (2006)

    Article  Google Scholar 

  12. Lerouge, J., Abu-Aisheh, Z., Raveaux, R., Héroux, P., Adam, S.: New binary linear programming formulation to compute the graph edit distance. Pattern Recogn. 72, 254–265 (2017)

    Article  Google Scholar 

  13. Moreno-García, C.F., Cortés, X., Serratosa, F.: A graph repository for learning error-tolerant graph matching. In: Robles-Kelly, A., Loog, M., Biggio, B., Escolano, F., Wilson, R. (eds.) S+SSPR 2016. LNCS, vol. 10029, pp. 519–529. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49055-7_46

    Chapter  Google Scholar 

  14. Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 5(1), 32–38 (1957)

    Article  MathSciNet  Google Scholar 

  15. Riesen, K.: Structural Pattern Recognition with Graph Edit Distance. Advances in Computer Vision and Pattern Recognition. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27252-8

    Book  MATH  Google Scholar 

  16. Riesen, K., Neuhaus, M., Bunke, H.: Bipartite graph matching for computing the edit distance of graphs. In: Escolano, F., Vento, M. (eds.) GbRPR 2007. LNCS, vol. 4538, pp. 1–12. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72903-7_1

    Chapter  MATH  Google Scholar 

  17. Serratosa, F.: Computation of graph edit distance: reasoning about optimality and speed-up. Image Vis. Comput. 40, 38–48 (2015)

    Article  Google Scholar 

  18. Stauffer, M., Tschachtli, T., Fischer, A., Riesen, K.: A survey on applications of bipartite graph edit distance. In: Foggia, P., Liu, C.-L., Vento, M. (eds.) GbRPR 2017. LNCS, vol. 10310, pp. 242–252. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58961-9_22

    Chapter  Google Scholar 

  19. Zeng, Z., Tung, A.K., Wang, J., Feng, J., Zhou, L.: Comparing stars: on approximating graph edit distance. Proc. VLDB Endow. 2(1), 25–36 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mostafa Darwiche .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Darwiche, M., Conte, D., Raveaux, R., T’kindt, V. (2019). Solving the Graph Edit Distance Problem with Variable Partitioning Local Search. In: Conte, D., Ramel, JY., Foggia, P. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2019. Lecture Notes in Computer Science(), vol 11510. Springer, Cham. https://doi.org/10.1007/978-3-030-20081-7_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20081-7_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20080-0

  • Online ISBN: 978-3-030-20081-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics