Choosing the number of groups in a latent stochastic block model for dynamic networks

Files in This Item:
File Description SizeFormat 
insight_publication.pdf742.12 kBAdobe PDFDownload
Title: Choosing the number of groups in a latent stochastic block model for dynamic networks
Authors: Rastelli, RiccardoLatouche, PierreFriel, Nial
Permanent link: http://hdl.handle.net/10197/10302
Date: 23-Mar-2017
Online since: 2019-05-07T09:30:16Z
Abstract: Latent stochastic block models are flexible statistical models that are widely used in social network analysis. In recent years, efforts have been made to extend these models to temporal dynamic networks, whereby the connections between nodes are observed at a number of different times. In this paper we extend the original stochastic block model by using a Markovian property to describe the evolution of nodes cluster memberships over time. We recast the problem of clustering the nodes of the network into a model-based context, and show that the integrated completed likelihood can be evaluated analytically for a number of likelihood models. Then, we propose a scalable greedy algorithm to maximise this quantity, thereby estimating both the optimal partition and the ideal number of groups in a single inferential framework. Finally we propose applications of our methodology to both real and artificial datasets.
Funding Details: Science Foundation Ireland
metadata.dc.description.othersponsorship: Insight Research Centre
Type of material: Journal Article
Publisher: Cambridge University Press
Journal: Network Science
Start page: 1
End page: 27
Keywords: Stochastic block modelsDynamics networksGreedy optimisationBayesian inferenceIntegrated completed likelihood
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

Show full item record

Page view(s)

158
Last Week
4
Last month
checked on Dec 7, 2019

Download(s)

55
checked on Dec 7, 2019

Google ScholarTM

Check


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.