Statistical Methods and Applications

, volume 30 , issue 5 , pages 1365-1398

Weighted stochastic block model

Tin Lok James Ng ¹

Thomas Brendan Murphy ²

Hide authors affiliations Show authors affiliations: 2 affiliations

School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland |

School of Mathematics and Statistics, University College Dublin, Dublin, Ireland |

Publication type: Journal Article

Publication date: 2021-09-13

Springer Nature

Statistical Methods and Applications

scimago Q3

wos Q3

SJR: 0.411

CiteScore: 2.3

Impact factor: 0.8

ISSN: 16182510, 1613981X

DOI: 10.1007/s10260-021-00590-6

Copy DOI

PubMed ID: 34840548

Statistics and Probability

Statistics, Probability and Uncertainty

Abstract

We propose a weighted stochastic block model (WSBM) which extends the stochastic block model to the important case in which edges are weighted. We address the parameter estimation of the WSBM by use of maximum likelihood and variational approaches, and establish the consistency of these estimators. The problem of choosing the number of classes in a WSBM is addressed. The proposed model is applied to simulated data and an illustrative data set.

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GOST Copy

Ng T. L. J., Murphy T. B. Weighted stochastic block model // Statistical Methods and Applications. 2021. Vol. 30. No. 5. pp. 1365-1398.

GOST all authors (up to 50) Copy

Ng T. L. J., Murphy T. B. Weighted stochastic block model // Statistical Methods and Applications. 2021. Vol. 30. No. 5. pp. 1365-1398.

RIS |

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RIS Copy

TY - JOUR

DO - 10.1007/s10260-021-00590-6

UR - https://doi.org/10.1007/s10260-021-00590-6

TI - Weighted stochastic block model

T2 - Statistical Methods and Applications

AU - Ng, Tin Lok James

AU - Murphy, Thomas Brendan

PY - 2021

DA - 2021/09/13

PB - Springer Nature

SP - 1365-1398

IS - 5

VL - 30

PMID - 34840548

SN - 1618-2510

SN - 1613-981X

ER -

BibTex |

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BibTex (up to 50 authors) Copy

@article{2021_Ng,

author = {Tin Lok James Ng and Thomas Brendan Murphy},

title = {Weighted stochastic block model},

journal = {Statistical Methods and Applications},

year = {2021},

volume = {30},

publisher = {Springer Nature},

month = {sep},

url = {https://doi.org/10.1007/s10260-021-00590-6},

number = {5},

pages = {1365--1398},

doi = {10.1007/s10260-021-00590-6}

}

MLA

Cite this

MLA Copy

Ng, Tin Lok James, and Thomas Brendan Murphy. “Weighted stochastic block model.” Statistical Methods and Applications, vol. 30, no. 5, Sep. 2021, pp. 1365-1398. https://doi.org/10.1007/s10260-021-00590-6.

Publisher

Springer Nature

Journal

Statistical Methods and Applications

scimago Q3

wos Q3

SJR

0.411

CiteScore

2.3

Impact factor

0.8

ISSN

16182510 (Print)

1613981X (Electronic)