Ren, S. orcid.org/0000-0001-9040-249X, Wang, X., Liu, P. orcid.org/0000-0002-0492-0029 et al. (1 more author) (2023) Bayesian nonparametric mixtures of Exponential Random Graph Models for ensembles of networks. Social Networks, 74. pp. 156-165. ISSN 0378-8733
Abstract
Ensembles of networks arise in various fields where multiple independent networks are observed, for example, a collection of student networks from different classes. However, there are few models that describe both the variations and characteristics of networks in an ensemble at the same time. In this manuscript, we propose to model ensembles of networks using a Dirichlet Process Mixture of Exponential Random Graph Models (DPM-ERGMs), which divides an ensemble into different clusters and models each cluster of networks using a separate Exponential Random Graph Model (ERGM). By employing a Dirichlet process mixture, the number of clusters can be determined automatically and changed adaptively with the data provided. Moreover, in order to perform full Bayesian inference for DPM-ERGMs, we develop a Metropolis-within-slice sampling algorithm to address the problem of sampling from the intractable ERGMs on an infinite sample space. We also demonstrate the performance of DPM-ERGMs with both simulated and real datasets.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Network clustering; Dirichlet process; Markov Chain Monte Carlo; Importance sampling; Adjusted pseudo likelihood |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 31 Mar 2023 08:38 |
Last Modified: | 31 Mar 2023 08:38 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.socnet.2023.03.005 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:197903 |