Fitting ERGMs on big networks.
The exponential random graph model (ERGM) has become a valuable tool for modeling social networks. In particular, ERGM provides great flexibility to account for both covariates effects on tie formations and endogenous network formation processes. However, there are both conceptual and computational...
| Publicado en: | Social Science Research Vol. 59; pp. 107 - 120 |
|---|---|
| Autor principal: | |
| Formato: | Artículo |
| Publicado: |
Academic Press Inc.
Sep2016
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=117269818&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 117269818 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0049089X SSS jtl: Social Science Research issn: 0049089X maglogo: N pubinfo: dt: Sep2016 vid: 59 pid: 735 pub: Academic Press Inc. artinfo: ui: 117269818 10.1016/j.ssresearch.2016.04.019 ppf: 107 ppct: 13 formats: tig: atl: Fitting ERGMs on big networks. aug: au: An, Weihua affil: Departments of Statistics and Sociology, Indiana University Bloomington, 752 Ballantine Hall, 1020 East Kirkwood Avenue, Bloomington, IN 47405, USA su: Social networks Software frameworks Algorithm software Subroutines (Computer programs) Computer network architectures sug: subj: Social networks Other Individual and Family Services Software frameworks Algorithm software Subroutines (Computer programs) Computer network architectures keyword: Big networks ERGMs Link tracing MCMLE Meta network analysis PMLE Big networks ERGMs Link tracing MCMLE Meta network analysis PMLE ab: The exponential random graph model (ERGM) has become a valuable tool for modeling social networks. In particular, ERGM provides great flexibility to account for both covariates effects on tie formations and endogenous network formation processes. However, there are both conceptual and computational issues for fitting ERGMs on big networks. This paper describes a framework and a series of methods (based on existent algorithms) to address these issues. It also outlines the advantages and disadvantages of the methods and the conditions to which they are most applicable. Selected methods are illustrated through examples. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|