Network Structure Change Point Detection by Posterior Predictive Discrepancy

Autor:	Georgy Sofronov, Tiangang Cui, Lingbin Bian, Jonathan M. Keith
Rok vydání:	2020
Předmět:	Computer science Monte Carlo method Bayesian probability Parameter space Bayesian inference computer.software_genre symbols.namesake ComputingMethodologies_PATTERNRECOGNITION Stochastic block model Sliding window protocol symbols Data mining computer Change detection Gibbs sampling
Zdroj:	Springer Proceedings in Mathematics & Statistics ISBN: 9783030434649
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8cc65c98dba2ba2457c6a532e736b493 https://doi.org/10.1007/978-3-030-43465-6_5 Zobrazit plný text záznamu