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    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-017-9788-9

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Dynamic stochastic block models: Parameter estimation and detection of changes in community structure

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Dynamic stochastic block models: Parameter estimation and detection of changes in community structure. / Ludkin, Matthew; Neal, Peter John; Eckley, Idris Arthur.
In: Statistics and Computing, Vol. 28, No. 6, 11.2018, p. 1201-1213.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Ludkin, M, Neal, PJ & Eckley, IA 2018, 'Dynamic stochastic block models: Parameter estimation and detection of changes in community structure', Statistics and Computing, vol. 28, no. 6, pp. 1201-1213. https://doi.org/10.1007/s11222-017-9788-9

APA

Ludkin, M., Neal, P. J., & Eckley, I. A. (2018). Dynamic stochastic block models: Parameter estimation and detection of changes in community structure. Statistics and Computing, 28(6), 1201-1213. https://doi.org/10.1007/s11222-017-9788-9

Vancouver

Ludkin M, Neal PJ, Eckley IA. Dynamic stochastic block models: Parameter estimation and detection of changes in community structure. Statistics and Computing. 2018 Nov;28(6):1201-1213. Epub 2017 Nov 2. doi: 10.1007/s11222-017-9788-9

Author

Ludkin, Matthew ; Neal, Peter John ; Eckley, Idris Arthur. / Dynamic stochastic block models : Parameter estimation and detection of changes in community structure. In: Statistics and Computing. 2018 ; Vol. 28, No. 6. pp. 1201-1213.

Bibtex

@article{c3b39822d30c48e7b3d74d7c830fcb44,
title = "Dynamic stochastic block models: Parameter estimation and detection of changes in community structure",
abstract = "The stochastic block model (SBM) is widely used for modelling network data by assigning individuals (nodes) to communities (blocks) with the probability of an edge existing between individuals depending upon community membership.In this paper we introduce an autoregressive extension of the SBM. This is based on continuous time Markovian edge dynamics. The model is appropriate for networks evolving over time and allows for edges to turn on and off. Moreover, we allow for the movement of individuals between communities. An effective reversible jump Markov chain Monte Carlo algorithm is introduced for sampling jointly from the posterior distribution of the community parameters and the number and location of changes in community membership. The algorithm is successfully applied to a network of mice.",
keywords = "Stochastic block model, Autoregressive dynamic network, Reversible-jump MCMC, Continuous-time network ",
author = "Matthew Ludkin and Neal, {Peter John} and Eckley, {Idris Arthur}",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-017-9788-9",
year = "2018",
month = nov,
doi = "10.1007/s11222-017-9788-9",
language = "English",
volume = "28",
pages = "1201--1213",
journal = "Statistics and Computing",
issn = "0960-3174",
publisher = "Springer Netherlands",
number = "6",

}

RIS

TY - JOUR

T1 - Dynamic stochastic block models

T2 - Parameter estimation and detection of changes in community structure

AU - Ludkin, Matthew

AU - Neal, Peter John

AU - Eckley, Idris Arthur

N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-017-9788-9

PY - 2018/11

Y1 - 2018/11

N2 - The stochastic block model (SBM) is widely used for modelling network data by assigning individuals (nodes) to communities (blocks) with the probability of an edge existing between individuals depending upon community membership.In this paper we introduce an autoregressive extension of the SBM. This is based on continuous time Markovian edge dynamics. The model is appropriate for networks evolving over time and allows for edges to turn on and off. Moreover, we allow for the movement of individuals between communities. An effective reversible jump Markov chain Monte Carlo algorithm is introduced for sampling jointly from the posterior distribution of the community parameters and the number and location of changes in community membership. The algorithm is successfully applied to a network of mice.

AB - The stochastic block model (SBM) is widely used for modelling network data by assigning individuals (nodes) to communities (blocks) with the probability of an edge existing between individuals depending upon community membership.In this paper we introduce an autoregressive extension of the SBM. This is based on continuous time Markovian edge dynamics. The model is appropriate for networks evolving over time and allows for edges to turn on and off. Moreover, we allow for the movement of individuals between communities. An effective reversible jump Markov chain Monte Carlo algorithm is introduced for sampling jointly from the posterior distribution of the community parameters and the number and location of changes in community membership. The algorithm is successfully applied to a network of mice.

KW - Stochastic block model

KW - Autoregressive dynamic network

KW - Reversible-jump MCMC

KW - Continuous-time network

U2 - 10.1007/s11222-017-9788-9

DO - 10.1007/s11222-017-9788-9

M3 - Journal article

VL - 28

SP - 1201

EP - 1213

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

IS - 6

ER -