Combinatorial Miller–Hagberg Algorithm for Randomization of Dense Networks

  • Hiroki Sayama
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


We propose a slightly revised Miller–Hagberg (MH) algorithm that efficiently generates a random network from a given expected degree sequence. The revision was to replace the approximated edge probability between a pair of nodes with a combinatorically calculated edge probability that better captures the likelihood of edge presence especially, where edges are dense. The computational complexity of this combinatorial MH algorithm is still in the same order as the original one. We evaluated the proposed algorithm through several numerical experiments. The results demonstrated that the proposed algorithm was particularly good at accurately representing high-degree nodes in dense, heterogeneous networks. This algorithm may be a useful alternative to other more established network randomization methods, given that the data are increasingly becoming larger and denser in today’s network science research.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Center for Collective Dynamics of Complex Systems and Department of Systems Science and Industrial EngineeringBinghamton UniversityBinghamtonUSA
  2. 2.Center for Complex Network ResearchNortheastern UniversityBostonUSA

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