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加权随机块模型

Weighted stochastic block model.

作者信息

Ng Tin Lok James, Murphy Thomas Brendan

机构信息

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

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

出版信息

Stat Methods Appt. 2021;30(5):1365-1398. doi: 10.1007/s10260-021-00590-6. Epub 2021 Sep 13.

DOI:10.1007/s10260-021-00590-6
PMID:34840548
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8608781/
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.

摘要

我们提出了一种加权随机块模型（WSBM），它将随机块模型扩展到边具有权重的重要情形。我们通过使用最大似然法和变分法来处理WSBM的参数估计问题，并建立这些估计量的一致性。还讨论了在WSBM中选择类数的问题。所提出的模型被应用于模拟数据和一个示例数据集。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e06/8608781/8ecfd2438dac/10260_2021_590_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e06/8608781/931f129fbb2b/10260_2021_590_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e06/8608781/812607fc9d49/10260_2021_590_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e06/8608781/8ecfd2438dac/10260_2021_590_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e06/8608781/931f129fbb2b/10260_2021_590_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e06/8608781/812607fc9d49/10260_2021_590_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e06/8608781/8ecfd2438dac/10260_2021_590_Fig3_HTML.jpg

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本文引用的文献

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Nonparametric weighted stochastic block models.非参数加权随机块模型。
Phys Rev E. 2018 Jan;97(1-1):012306. doi: 10.1103/PhysRevE.97.012306.
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Stochastic blockmodels with a growing number of classes.具有不断增加类别的随机块模型。
Biometrika. 2012 Jun;99(2):273-284. doi: 10.1093/biomet/asr053. Epub 2012 Apr 17.
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Mixed Membership Stochastic Blockmodels.混合成员随机块模型
估计加权网络中的社区数量。
Entropy (Basel). 2023 Mar 23;25(4):551. doi: 10.3390/e25040551.
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Degree-corrected distribution-free model for community detection in weighted networks.无向加权网络社团检测的度修正无分布模型。
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Compartmentalization increases food-web persistence.分区化提高了食物网的持久性。
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Analysis of weighted networks.加权网络分析
Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Nov;70(5 Pt 2):056131. doi: 10.1103/PhysRevE.70.056131. Epub 2004 Nov 24.
9
The architecture of complex weighted networks.复杂加权网络的架构
Proc Natl Acad Sci U S A. 2004 Mar 16;101(11):3747-52. doi: 10.1073/pnas.0400087101. Epub 2004 Mar 8.