Skip to main content
Log in

Generating random connected planar graphs

  • Published:
GeoInformatica Aims and scope Submit manuscript

Abstract

Connected planar graphs are of interest to a variety of scholars. Being able to simulate a database of such graphs with selected properties would support specific types of inference for spatial analysis and other network-based disciplines. This paper presents a simple, efficient, and flexible connected planar graph generator for this purpose. It also summarizes a comparison between an empirical set of specimen graphs and their simulated counterpart set, and establishes evidence for positing a conjecture about the principal eigenvalue of connected planar graphs. Finally, it summarizes a probability assessment of the algorithm’s outcomes as well as a comparison between the new algorithm and selected existing planar graph generators.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. This upper bound on n is a restriction set by the Fortran compiler used. It can be relaxed by changing the computer software for implementation (see supplementary material for the Fortran code).

References

  1. Bodirsky M, Gröpl C, Kang M (2007) Generating labeled planar graphs uniformly at random. Theor Comput Sci 379:377–386

    Article  Google Scholar 

  2. S. Meinert and D. Wagner, (2011) An experimental study on generating planar graphs, Karlsruhe Reports in Informatics,13, Karlsruhe Institute of Technology, Faculty of Informatics Karlsruhe, Germany, 2011

  3. Osthus D, Prömel H, Taraz A (2003) On random planar graphs, the number of planar graphs and their triangulations. Journal of Combinatorial Theory, Series B 88:119–134

    Article  Google Scholar 

  4. Bonichon N, Gavoille C, Hanusse N, Poulalhon D, Schaeffer G (2006) Planar graphs, via well-orderly maps and trees. Graphs and Combinatorics 22:185–202

    Article  Google Scholar 

  5. O. Giménez and M. Noy. “The number of planar graphs and properties of random planar graphs,” Proceedings of the 2005 International Conference on Analysis of Algorithms C. Saunders, M. Grobelnik, S. Gunn, and J. Shawe-Taylor (editors), Springer-Verlag, Berlin (2005) pp. 147–156

  6. R. Read and R. Wilson (2005) An Atlas of Graphs, Clarendon press, Gloucestershire, England

  7. Geographical Analysis, (2011) Issue 4, 43, 345–435

  8. Fortin M-J, James P, MacKenzie A, Melles S, Rayfield B (2012) Spatial statistics, spatial regression, and graph theory in economy. Spat Stat 1:100–109

    Article  Google Scholar 

  9. Boots B, Royal G (1991) A conjecture on the maximum value of the principal eigenvalue of a planar graph. Geogr Anal 23:276–282

    Article  Google Scholar 

  10. M. Tait and J. Tobin, (2016) Three conjectures in extremal spectral graph theory, arXiv:1606.01916v1 [math.CO], last accessed on 15 December 2016

  11. Páez A, Scott D, Volz E (2008) Weight matrices for social influence analysis: an investigation of measurement errors and their effect on model identification and estimation quality. Soc Networks 30:300–317

    Article  Google Scholar 

  12. Griffith D (2017) Some robust assessments of Moran eigenvector spatial filtering. Spatial Statistics 22:155–179

    Article  Google Scholar 

  13. Masucci A, Smith D, Crooks A, Batty M (2009) Random planar graphs and the London street network. The European Physical Journal B 71:259–271

    Article  Google Scholar 

  14. Ermagun A, Levinson D (2018) An introduction to the network weight matrix. Geogr Anal 50:76–96

    Article  Google Scholar 

  15. D. Bauman, T. Drouet, M-J Fortin and S. Dray, (2018) Optimizing the choice of a spatial weighting matrix in eigenvector-based methods. Ecology, available at https://doi.org/10.1002/ecy.2469 (last accessed on 18 August 2018)

    Article  Google Scholar 

  16. S. Arlinghaus, W. Arlinghaus, and F. Harary. (2002) Graph Theory and Geography: An Interactive View, Wiley, New York

  17. G. Brinkmann, (n.d.) fullgen Manual, https://users.cecs.anu.edu.au/~bdm/plantri/fullgen-guide.txt, last accessed on 22 February 2017

  18. E. Allender and M. Mahajan, The complexity of planarity testing. Inf Comput 189 (2004), 117–134

    Article  Google Scholar 

  19. F. Harary and E. Palmer. 1973. Graphical enumeration. NY: Academic Press

    Google Scholar 

  20. Griffith D, Sone A (1995) Trade-offs associated with normalizing constant computational simplifications for estimating spatial statistical models. J Stat Comput Simul 51:165–183

    Article  Google Scholar 

  21. Griffith D (2000) Eigenfunction properties and approximations of selected incidence matrices employed in spatial analyses. Linear Algebra Appl 321:95–112

    Article  Google Scholar 

  22. Wilf H (1967) The eigenvalues of a graph and its chromatic number. J Lond Math Soc 42:330–332

    Article  Google Scholar 

  23. Hoffman A (1970) “On eigenvalues and colorings of graphs,” Graph Theory and Its Applications H. Bernard (editor). Academic Press, New York, pp 79–92

    Google Scholar 

  24. G. Brinkmann and B. McKay, (2011) Guide for Using plantri (version 4.5), http://users.cecs.anu.edu.au/~bdm/plantri/plantri-guide.txt, last accessed on 22 February 2017

  25. G. Brinkmann and B. McKay, (2012) Guide for Using buckygen (version 1.0), http://caagt.ugent.be/buckygen/buckygen-guide.txt, last accessed on 22 February 2017

  26. Anderson T (1963) Asymptotic theory for principal components analysis. Ann Math Stat 34:122–148

    Article  Google Scholar 

  27. Johnstone I (2001) On the distribution of the largest eigenvalue in principal components analysis. Ann Stat 29:295–327

    Article  Google Scholar 

Download references

Acknowledgments

Dr. Daniel A. Griffith is an Ashbel Smith Professor of Geospatial Information Sciences.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daniel A. Griffith.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(TXT 13 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Griffith, D.A. Generating random connected planar graphs. Geoinformatica 22, 767–782 (2018). https://doi.org/10.1007/s10707-018-0328-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10707-018-0328-3

Keywords

Navigation