Combinatorial miller–hagberg algorithm for randomization of dense networks

Research output: Contribution to journalConference article

Abstract

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
Pages (from-to)65-73
Number of pages9
JournalSpringer Proceedings in Complexity
Issue number219279
DOIs
Publication statusPublished - 2018 Jan 1
Externally publishedYes
Event9th International Conference on Complex Networks, CompleNet 2018 - Boston, United States
Duration: 2018 Mar 52018 Mar 8

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Combinatorial Algorithms
Randomisation
Degree Sequence
Heterogeneous Networks
Random Networks
Vertex of a graph
Heterogeneous networks
Likelihood
Computational Complexity
Numerical Experiment
Computational complexity
Alternatives
Experiments

ASJC Scopus subject areas

  • Applied Mathematics
  • Modelling and Simulation
  • Computer Science Applications

Cite this

Combinatorial miller–hagberg algorithm for randomization of dense networks. / Sayama, Hiroki.

In: Springer Proceedings in Complexity, No. 219279, 01.01.2018, p. 65-73.

Research output: Contribution to journalConference article

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