### 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 language | English |
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Pages (from-to) | 65-73 |

Number of pages | 9 |

Journal | Springer Proceedings in Complexity |

Issue number | 219279 |

DOIs | |

Publication status | Published - 2018 Jan 1 |

Externally published | Yes |

Event | 9th International Conference on Complex Networks, CompleNet 2018 - Boston, United States Duration: 2018 Mar 5 → 2018 Mar 8 |

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### 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.

Research output: Contribution to journal › Conference article

}

TY - JOUR

T1 - Combinatorial miller–hagberg algorithm for randomization of dense networks

AU - Sayama, Hiroki

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85054712144&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054712144&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-73198-8_6

DO - 10.1007/978-3-319-73198-8_6

M3 - Conference article

AN - SCOPUS:85054712144

SP - 65

EP - 73

JO - Springer Proceedings in Complexity

JF - Springer Proceedings in Complexity

SN - 2213-8684

IS - 219279

ER -