Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Graph Edit Distance Compacted Search Tree

  • Conference paper
  • First Online:
Similarity Search and Applications (SISAP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13590))

Included in the following conference series:

  • 810 Accesses

Abstract

We propose two methods to compact the used search tree during the graph edit distance (GED) computation. The first maps the node information and encodes the different edit operations by numbers and the needed remaining vertices and edges by BitSets. The second represents the tree succinctly by bit-vectors. The proposed methods require 24 to 250 times less memory than traditional versions without negatively influencing the running time.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Notes

  1. 1.

    Download from https://gdc2016.greyc.fr/#ged.

  2. 2.

    These settings were used in the competition https://gdc2016.greyc.fr/#ged.

  3. 3.

    MemoryMeter: https://github.com/jbellis/jamm.

References

  1. Abu-Aisheh, Z., Raveaux, R., Ramel, J.Y., Martineau, P.: An exact graph edit distance algorithm for solving pattern recognition problems. In: 4th International Conference on Pattern Recognition Applications and Methods 2015 (2015)

    Google Scholar 

  2. Blumenthal, D.B., Gamper, J.: Correcting and speeding-up bounds for non-uniform graph edit distance. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 131–134. IEEE (2017)

    Google Scholar 

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

    Article  Google Scholar 

  4. Bunke, H., Riesen, K.: Recent advances in graph-based pattern recognition with applications in document analysis. Pattern Recogn. 44(5), 1057–1067 (2011)

    Article  Google Scholar 

  5. Chambi, S., Lemire, D., Kaser, O., Godin, R.: Better bitmap performance with roaring bitmaps. Softw. Pract. Exp. 46(5), 709–719 (2016)

    Article  Google Scholar 

  6. Dabah, A., Chegrane, I., Yahiaoui, S.: Efficient approximate approach for graph edit distance problem. Pattern Recognit. Lett. 151, 310–316 (2021)

    Article  Google Scholar 

  7. Gouda, K., Hassaan, M.: Csi_ged: an efficient approach for graph edit similarity computation. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 265–276. IEEE (2016)

    Google Scholar 

  8. 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 /SPR 2006. LNCS, vol. 4109, pp. 163–172. Springer, Heidelberg (2006). https://doi.org/10.1007/11815921_17

    Chapter  Google Scholar 

  9. Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image Vis. Comput. 27(7), 950–959 (2009)

    Article  Google Scholar 

  10. Riesen, K., Fankhauser, S., Bunke, H.: Speeding up graph edit distance computation with a bipartite heuristic. In: MLG (2007)

    Google Scholar 

  11. Riesen, K., Fischer, A., Bunke, H.: Computing upper and lower bounds of graph edit distance in cubic time. In: El Gayar, N., Schwenker, F., Suen, C. (eds.) ANNPR 2014. LNCS (LNAI), vol. 8774, pp. 129–140. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11656-3_12

    Chapter  Google Scholar 

  12. Vento, M.: A long trip in the charming world of graphs for pattern recognition. Pattern Recogn. 48(2), 291–301 (2015)

    Article  Google Scholar 

  13. 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 Ibrahim Chegrane .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chegrane, I., Hocine, I., Yahiaoui, S., Bendjoudi, A., Nouali-Taboudjemat, N. (2022). Graph Edit Distance Compacted Search Tree. In: Skopal, T., Falchi, F., Lokoč, J., Sapino, M.L., Bartolini, I., Patella, M. (eds) Similarity Search and Applications. SISAP 2022. Lecture Notes in Computer Science, vol 13590. Springer, Cham. https://doi.org/10.1007/978-3-031-17849-8_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-17849-8_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17848-1

  • Online ISBN: 978-3-031-17849-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics