Combinatorial miller–hagberg algorithm for randomization of dense networks

Hiroki Sayama*

*この研究の対応する著者

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルSpringer Proceedings in Complexity
編集者Sean Cornelius, Kate Coronges, Bruno Goncalves, Roberta Sinatra, Alessandro Vespignani
出版社Springer Science and Business Media B.V.
ページ65-73
ページ数9
ISBN(印刷版)9783319731971
DOI
出版ステータスPublished - 2018
外部発表はい
イベント9th International Conference on Complex Networks, CompleNet 2018 - Boston, United States
継続期間: 2018 3月 52018 3月 8

出版物シリーズ

名前Springer Proceedings in Complexity
0
ISSN(印刷版)2213-8684
ISSN(電子版)2213-8692

Other

Other9th International Conference on Complex Networks, CompleNet 2018
国/地域United States
CityBoston
Period18/3/518/3/8

ASJC Scopus subject areas

  • 応用数学
  • モデリングとシミュレーション
  • コンピュータ サイエンスの応用

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