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Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models

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Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models. / Ludkin, M.
In: Computational Statistics and Data Analysis, Vol. 152, 107051, 01.12.2020.

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

Harvard

Ludkin, M 2020, 'Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models', Computational Statistics and Data Analysis, vol. 152, 107051. https://doi.org/10.1016/j.csda.2020.107051

APA

Ludkin, M. (2020). Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models. Computational Statistics and Data Analysis, 152, Article 107051. https://doi.org/10.1016/j.csda.2020.107051

Vancouver

Ludkin M. Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models. Computational Statistics and Data Analysis. 2020 Dec 1;152:107051. Epub 2020 Jul 24. doi: 10.1016/j.csda.2020.107051

Author

Ludkin, M. / Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models. In: Computational Statistics and Data Analysis. 2020 ; Vol. 152.

Bibtex

@article{2602636a686f4ef1b338d811cc402f68,
title = "Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models",
abstract = "The stochastic block model (SBM) is a popular model for capturing community structure and interaction within a network. Network data with non-Boolean edge weights is becoming commonplace; however, existing analysis methods convert such data to a binary representation to apply the SBM, leading to a loss of information. A generalisation of the SBM is considered, which allows edge weights to be modelled in their recorded state. An effective reversible jump Markov chain Monte Carlo sampler is proposed for estimating the parameters and the number of blocks for this generalised SBM. The methodology permits non-conjugate distributions for edge weights, which enable more flexible modelling than current methods as illustrated on synthetic data, a network of brain activity and an email communication network. ",
keywords = "Network, Non-conjugate analysis, Statistical analysis of network data, Stochastic block model, Brain, Data handling, Markov chains, Stochastic systems, Analysis method, Binary representations, Brain activity, Community structures, Number of blocks, Reversible jump Markov chain Monte Carlo, Stochastic block models, Synthetic data, Stochastic models",
author = "M. Ludkin",
year = "2020",
month = dec,
day = "1",
doi = "10.1016/j.csda.2020.107051",
language = "English",
volume = "152",
journal = "Computational Statistics and Data Analysis",
issn = "0167-9473",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models

AU - Ludkin, M.

PY - 2020/12/1

Y1 - 2020/12/1

N2 - The stochastic block model (SBM) is a popular model for capturing community structure and interaction within a network. Network data with non-Boolean edge weights is becoming commonplace; however, existing analysis methods convert such data to a binary representation to apply the SBM, leading to a loss of information. A generalisation of the SBM is considered, which allows edge weights to be modelled in their recorded state. An effective reversible jump Markov chain Monte Carlo sampler is proposed for estimating the parameters and the number of blocks for this generalised SBM. The methodology permits non-conjugate distributions for edge weights, which enable more flexible modelling than current methods as illustrated on synthetic data, a network of brain activity and an email communication network.

AB - The stochastic block model (SBM) is a popular model for capturing community structure and interaction within a network. Network data with non-Boolean edge weights is becoming commonplace; however, existing analysis methods convert such data to a binary representation to apply the SBM, leading to a loss of information. A generalisation of the SBM is considered, which allows edge weights to be modelled in their recorded state. An effective reversible jump Markov chain Monte Carlo sampler is proposed for estimating the parameters and the number of blocks for this generalised SBM. The methodology permits non-conjugate distributions for edge weights, which enable more flexible modelling than current methods as illustrated on synthetic data, a network of brain activity and an email communication network.

KW - Network

KW - Non-conjugate analysis

KW - Statistical analysis of network data

KW - Stochastic block model

KW - Brain

KW - Data handling

KW - Markov chains

KW - Stochastic systems

KW - Analysis method

KW - Binary representations

KW - Brain activity

KW - Community structures

KW - Number of blocks

KW - Reversible jump Markov chain Monte Carlo

KW - Stochastic block models

KW - Synthetic data

KW - Stochastic models

U2 - 10.1016/j.csda.2020.107051

DO - 10.1016/j.csda.2020.107051

M3 - Journal article

VL - 152

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

M1 - 107051

ER -