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  5. A multilayer exponential random graph modelling approach for weighted networks
 
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A multilayer exponential random graph modelling approach for weighted networks

Author(s)
Caimo, Alberto 
Gollini, Isabella 
Uri
http://hdl.handle.net/10197/10992
Date Issued
February 2020
Date Available
19T10:21:23Z August 2019
Abstract
A new modelling approach for the analysis of weighted networks with ordinal/polytomous dyadic values is introduced. Specifically, it is proposed to model the weighted network connectivity structure using a hierarchical multilayer exponential random graph model (ERGM) generative process where each network layer represents a different ordinal dyadic category. The network layers are assumed to be generated by an ERGM process conditional on their closest lower network layers. A crucial advantage of the proposed method is the possibility of adopting the binary network statistics specification to describe both the between-layer and across-layer network processes and thus facilitating the interpretation of the parameter estimates associated to the network effects included in the model. The Bayesian approach provides a natural way to quantify the uncertainty associated to the model parameters. From a computational point of view, an extension of the approximate exchange algorithm is proposed to sample from the doubly-intractable parameter posterior distribution. A simulation study is carried out on artificial data and applications of the methodology are illustrated on well-known datasets. Finally, a goodness-of-fit diagnostic procedure for model assessment is proposed.
Type of Material
Journal Article
Publisher
Elsevier
Journal
Computational Statistics & Data Analysis
Volume
142
Start Page
1
End Page
18
Copyright (Published Version)
2019 Elsevier
Keywords
  • Statistical network m...

  • Weighted networks

  • Bayesian analysis

  • Intractable models

DOI
10.1016/j.csda.2019.106825
Language
English
Status of Item
Peer reviewed
ISSN
0167-9473
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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CSDA_2019.pdf

Size

3.68 MB

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Owning collection
Mathematics and Statistics Research Collection
Scopus© citations
14
Acquisition Date
Nov 30, 2023
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651
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Downloads
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