Do greedy assortativity optimization algorithms produce good results?

  • W. Winterbach
  • D. de Ridder
  • H. J. Wang
  • M. Reinders
  • P. Van Mieghem
Regular Article

Abstract

We consider algorithms for generating networks that are extremal with respect to degree assortativity. Networks with maximized and minimized assortativities have been studied by other authors. In these cases, networks are rewired whilst maintaining their degree vectors. Although rewiring can be used to create networks with high or low assortativities, it is not known how close the results are to the true maximum or minimum assortativities achievable by networks with the same degree vectors. We introduce the first algorithm for computing a network with maximal or minimal assortativity on a given vector of valid node degrees. We compare the assortativity metrics of networks obtained by this algorithm to assortativity metrics of networks obtained by a greedy assortativity-maximization algorithm. The algorithms are applied to Erdős-Rényi networks, Barabàsi-Albert and a sample of real-world networks. We find that the number of rewirings considered by the greedy approach must scale with the number of links in order to ensure a good approximation.

Keywords

Statistical and Nonlinear Physics 

Supplementary material

10051_2012_477_MOESM1_ESM.pdf (120 kb)
Supplementary material, approximately 119 KB.

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Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • W. Winterbach
    • 1
    • 2
  • D. de Ridder
    • 2
  • H. J. Wang
    • 1
  • M. Reinders
    • 2
  • P. Van Mieghem
    • 1
  1. 1.Network Architecture and ServicesDelft University of Technology, Faculty of EEMCSDelftNetherlands
  2. 2.The Delft Bioinformatics LabDelft University of Technology, Faculty of EEMCSDelftNetherlands

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