Network structure change point detection by posterior predictive discrepancy

Lingbin Bian*, Tiangang Cui, Georgy Sofronov, Jonathan Keith

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

Detecting changes in network structure is important for research into systems as diverse as financial trading networks, social networks and brain connectivity. Here we present novel Bayesian methods for detecting network structure change points. We use the stochastic block model to quantify the likelihood of a network structure and develop a score we call posterior predictive discrepancy based on sliding windows to evaluate the model fitness to the data. The parameter space for this model includes unknown latent label vectors assigning network nodes to interacting communities. Monte Carlo techniques based on Gibbs sampling are used to efficiently sample the posterior distributions over this parameter space.
Original languageEnglish
Title of host publicationMonte Carlo and Quasi-Monte Carlo Methods
Subtitle of host publicationMCQMC 2018, Rennes, France, July 1–6
EditorsBruno Tuffin, Pierre L'Ecuyer
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages107-123
Number of pages17
ISBN (Electronic)9783030434656
ISBN (Print)9783030434649
DOIs
Publication statusPublished - 2020
EventInternational Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (13th : 2018) - Rennes, France
Duration: 1 Jul 20186 Jul 2018
Conference number: 13th

Publication series

NameSpringer Proceedings in Mathematics and Statistics
PublisherSpringer
Volume324
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017

Conference

ConferenceInternational Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (13th : 2018)
Abbreviated titleMCQMC 2018
CountryFrance
CityRennes
Period1/07/186/07/18

Keywords

  • Bayesian inference
  • Networks
  • Sliding window
  • Stochastic block model
  • Gibbs sampling

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