Finding Biologically Plausible Complex Network Topologies with a New Evolutionary Approach for Network Generation

  • Gordon Govan
  • Jakub Chlanda
  • David Corne
  • Alex Xenos
  • Pierluigi Frisco
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 227)


We explore the recently introduced Structured Nodes (SN) network model, for which earlier work has shown its capability in matching several topological properties of complex networks. We consider a diverse set of empirical biological complex networks as targets and we use an evolutionary algorithm (EA) approach to identify input for the SN model allowing it to generate networks similar to these targets.

We find that by using the EA the structural fit between SN networks and the targets is improved.

The combined SN/EA approach is a promising direction to further investigate the growth, properties and behaviour of biological networks.


Path Length Degree Distribution Average Degree Topological Feature Average Cluster 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., Walter, P.: Molecular Biology of the Cell, 4th edn. Garland Science (2002)Google Scholar
  2. 2.
    Amaral, L.A.N., Scala, A., Barthélémy, M., Stanley, H.E.: Classes of small-world networks. Proceedings of the National Academy of Sciences 97(21), 11149–11152 (2000)CrossRefGoogle Scholar
  3. 3.
    Barabàsi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Barrat, A., Weigt, M.: On the properties of small-world network models. Eur. Phy. Jour. B - Condensed Matter and Complex Systems 13(3), 547–560 (2000)CrossRefGoogle Scholar
  6. 6.
    Bassett, D.S., Bullmore, E.: Small-world brain networks. The Neuroscientist 12(6), 512–523 (2006)CrossRefGoogle Scholar
  7. 7.
    Chung, F., Lu, L., Dewey, T.G., Galas, D.J.: Duplication models for biological networks. Journal of Computational Biology 10(5), 677–687 (2003)CrossRefGoogle Scholar
  8. 8.
    Coello, C.A.C., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems. In: Genetic and Evolutionary Computation, Springer-Verlag New York, Inc. (2006)Google Scholar
  9. 9.
    Cole, M.: Bringing skeletons out of the closet: a pragmatic manifesto for skeletal parallel programming. Parallel Computing 30(3), 389–406 (2004)CrossRefGoogle Scholar
  10. 10.
    Erdos, P., Renyi, A.: On random graphs I. Publicationes Mathematicae 6, 290–297 (1959)MathSciNetGoogle Scholar
  11. 11.
    Frisco, P.: Network model with structured nodes. Phys. Rev. E 84, 021931 (2011)CrossRefGoogle Scholar
  12. 12.
    He, Y., Chen, Z.J., Evans, A.C.: Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb. Cortex 17(10), 2407–2419 (2007)CrossRefGoogle Scholar
  13. 13.
    Junker, B.H., Schreiber, F. (eds.): Analysis of Biological Networks. Wiley-Blackwell (2008)Google Scholar
  14. 14.
    Kleinberg, J., Easley, D.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press (2011)Google Scholar
  15. 15.
    Maslov, S., Sneppen, K.: Specificity and stability in topology of protein networks. Science 296(5569), 910–913 (2002)CrossRefGoogle Scholar
  16. 16.
    Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: Simple building blocks of complex networks. Science 298(5594), 824–827 (2002)CrossRefGoogle Scholar
  17. 17.
    Sporns, O., Zwi, J.D.: The small world of the cerebral cortex. Neuroinformatics 2(2), 145–162 (2004)CrossRefGoogle Scholar
  18. 18.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Gordon Govan
    • 1
  • Jakub Chlanda
    • 1
  • David Corne
    • 1
  • Alex Xenos
    • 1
  • Pierluigi Frisco
    • 1
  1. 1.School of Mathematical and Computer SciencesHeriot-Watt UniversityEdinburghUK

Personalised recommendations