Bayesian analysis for exponential random graph models using the adaptive exchange sampler

Handle URI:
http://hdl.handle.net/10754/597647
Title:
Bayesian analysis for exponential random graph models using the adaptive exchange sampler
Authors:
Jin, Ick Hoon; Liang, Faming; Yuan, Ying
Abstract:
Exponential random graph models have been widely used in social network analysis. However, these models are extremely difficult to handle from a statistical viewpoint, because of the existence of intractable normalizing constants. In this paper, we consider a fully Bayesian analysis for exponential random graph models using the adaptive exchange sampler, which solves the issue of intractable normalizing constants encountered in Markov chain Monte Carlo (MCMC) simulations. The adaptive exchange sampler can be viewed as a MCMC extension of the exchange algorithm, and it generates auxiliary networks via an importance sampling procedure from an auxiliary Markov chain running in parallel. The convergence of this algorithm is established under mild conditions. The adaptive exchange sampler is illustrated using a few social networks, including the Florentine business network, molecule synthetic network, and dolphins network. The results indicate that the adaptive exchange algorithm can produce more accurate estimates than approximate exchange algorithms, while maintaining the same computational efficiency.
Citation:
Jin IH, Liang F, Yuan Y (2013) Bayesian analysis for exponential random graph models using the adaptive exchange sampler. Statistics and Its Interface 6: 559–576. Available: http://dx.doi.org/10.4310/sii.2013.v6.n4.a13.
Publisher:
International Press of Boston
Journal:
Statistics and Its Interface
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
2013
DOI:
10.4310/sii.2013.v6.n4.a13
PubMed ID:
24653788
Type:
Article
ISSN:
1938-7989; 1938-7997
Sponsors:
Yuan and Jin acknowledge support from the NIH grant R01CA154591.Liang’s research was partially supported by grants from the NationalScience Foundation (DMS-1007457, DMS-1106494 and DMS-1317131)and the award (KUS-C1-016-04) made by King Abdullah Universityof Science and Technology (KAUST)
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Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorJin, Ick Hoonen
dc.contributor.authorLiang, Famingen
dc.contributor.authorYuan, Yingen
dc.date.accessioned2016-02-25T12:43:40Zen
dc.date.available2016-02-25T12:43:40Zen
dc.date.issued2013en
dc.identifier.citationJin IH, Liang F, Yuan Y (2013) Bayesian analysis for exponential random graph models using the adaptive exchange sampler. Statistics and Its Interface 6: 559–576. Available: http://dx.doi.org/10.4310/sii.2013.v6.n4.a13.en
dc.identifier.issn1938-7989en
dc.identifier.issn1938-7997en
dc.identifier.pmid24653788en
dc.identifier.doi10.4310/sii.2013.v6.n4.a13en
dc.identifier.urihttp://hdl.handle.net/10754/597647en
dc.description.abstractExponential random graph models have been widely used in social network analysis. However, these models are extremely difficult to handle from a statistical viewpoint, because of the existence of intractable normalizing constants. In this paper, we consider a fully Bayesian analysis for exponential random graph models using the adaptive exchange sampler, which solves the issue of intractable normalizing constants encountered in Markov chain Monte Carlo (MCMC) simulations. The adaptive exchange sampler can be viewed as a MCMC extension of the exchange algorithm, and it generates auxiliary networks via an importance sampling procedure from an auxiliary Markov chain running in parallel. The convergence of this algorithm is established under mild conditions. The adaptive exchange sampler is illustrated using a few social networks, including the Florentine business network, molecule synthetic network, and dolphins network. The results indicate that the adaptive exchange algorithm can produce more accurate estimates than approximate exchange algorithms, while maintaining the same computational efficiency.en
dc.description.sponsorshipYuan and Jin acknowledge support from the NIH grant R01CA154591.Liang’s research was partially supported by grants from the NationalScience Foundation (DMS-1007457, DMS-1106494 and DMS-1317131)and the award (KUS-C1-016-04) made by King Abdullah Universityof Science and Technology (KAUST)en
dc.publisherInternational Press of Bostonen
dc.subjectAdaptive markov chain monte carloen
dc.subjectExchange algorithmen
dc.subjectExponential random graph modelen
dc.subjectSocial networken
dc.titleBayesian analysis for exponential random graph models using the adaptive exchange sampleren
dc.typeArticleen
dc.identifier.journalStatistics and Its Interfaceen
dc.contributor.institutionDepartment of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A.en
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, Texas, U.S.A.en
kaust.grant.numberKUS-C1-016-04en

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