Combinatorial miller–hagberg algorithm for randomization of dense networks

Hiroki Sayama*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationSpringer Proceedings in Complexity
EditorsSean Cornelius, Kate Coronges, Bruno Goncalves, Roberta Sinatra, Alessandro Vespignani
PublisherSpringer Science and Business Media B.V.
Number of pages9
ISBN (Print)9783319731971
Publication statusPublished - 2018
Externally publishedYes
Event9th International Conference on Complex Networks, CompleNet 2018 - Boston, United States
Duration: 2018 Mar 52018 Mar 8

Publication series

NameSpringer Proceedings in Complexity
ISSN (Print)2213-8684
ISSN (Electronic)2213-8692


Other9th International Conference on Complex Networks, CompleNet 2018
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Applied Mathematics
  • Modelling and Simulation
  • Computer Science Applications


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