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000841088 010__ $$z  2012021034
000841088 020__ $$z9780521193566
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000841088 020__ $$z9781139844321$$q(electronic book)
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000841088 050_4 $$aHM741$$b.E96 2013
000841088 08204 $$a302.3$$223
000841088 24500 $$aExponential random graph models for social networks$$h[electronic resource] :$$btheories, methods, and applications /$$ceditors, Dean Lusher, Johan Koskinen, Garry Robbins.
000841088 260__ $$aCambridge :$$bCambridge University Press,$$c2013.
000841088 300__ $$axxii, 336 p. :$$bill.
000841088 440_0 $$aStructural analysis in the social sciences ;$$v35
000841088 504__ $$aIncludes bibliographical references and index.
000841088 5058_ $$aMachine generated contents note: Introduction Dean Lusher, Johan Koskinen and Garry Robins; 1. What are exponential random graph models Garry Robins and Dean Lusher; 2. The formation of social network structure Dean Lusher and Garry Robins; 3. A simplified account of ERGM as a statistical model Garry Robins and Dean Lusher; 4. An example of ERGM analysis Dean Lusher and Garry Robins; 5. Exponential random graph model fundamentals Johan Koskinene and Galina Daragonova; 6. Dependence graphs and sufficient statistics Johan Koskinen and Galina Daragonova; 7. Social selection, dyadic covariates and geospatial effects Garry Robins and Galina Daragonova; 8. Autologistic actor attribute models Galina Daragonova and Garry Robins; 9. ERGM extensions: models for multiple networks and bipartite networks Peng Wang; 10. Longitudinal models Tom Snijders and Johan Koskinen; 11. Simulation, estimation and goodness of fit Johan Koskinen and Tom Snijders; 12. Illustrations: simulation, estimation and goodness of fit Garry Robins and Dean Lusher; 13. Personal attitudes, perceived attitudes and social structures: a social selection model Dean Lusher and Garry Robins; 14. How to close a hole: exploring alternative closure mechanisms in inter-organizational networks Alessandro Lomi and Francesca Pallotti; 15. Interdependencies between working relations: multivariate ERGMs for advice and satisfaction Yu Zhao and Olaf Rank; 16. Brain, brawn or optimism? The structure and correlates of emergent military leadership Yuval Kalish and Gil Luria; 17. An ALAAM analysis of unemployment: the dual importance of who you know and where you live Galina Daragonova and Philippa Pattison; 18. Longitudinal changes in face-to-face and text message-mediated friendship networks Tasuku Igarashi; 19. The differential impact of directors' social and financial capital on corporate interlock formation Nicholas Harrigan and Matthew Bond; 20. Comparing networks: a structural correspondence between behavioural and recall networks Eric Quintane; 21. Modelling social networks: next steps Philippa Pattison and Tom Snijders.
000841088 506__ $$aAccess limited to authorized users.
000841088 520__ $$a"Exponential random graph models (ERGMs) are a class of statistical models for social networks. They account for the presence (and absence) of network ties and so provide a model for network structure. An ERGM models a given network in terms of small local tie-based structures, such as reciprocated ties and triangles. A social network can be thought of as being built up of these local patterns of ties, called network configurations xe "network configurations" , which correspond to the parameters in the model. Moreover, these configurations can be considered to arise from local social processes, whereby actors in the network form connections in response to other ties in their social environment. ERGMs are a principled statistical approach to modeling social networks. They are theory-driven in that their use requires the researcher to consider the complex, intersecting and indeed potentially competing theoretical reasons why the social ties in the observed network have arisen. For instance, does a given network structure occur due to processes of homophily xe "actor-relation effects:homophily" , xe "homophily" \t "see actor-relation effects" reciprocity xe "reciprocity" , transitivity xe "transitivity" , or indeed a combination of these? By including such parameters together in the one model a researcher can test these effects one against the other, and so infer the social processes that have built the network. Being a statistical model, an ERGM permits inferences about whether, in our network of interest, there are significantly more (or fewer) reciprocated ties, or triangles (for instance), than we would expect"--$$cProvided by publisher.
000841088 650_0 $$aSocial networks$$xMathematical models.
000841088 650_0 $$aSocial networks$$xResearch$$xGraphic methods.
000841088 7001_ $$aLusher, Dean.
000841088 7001_ $$aKoskinen, Johan.
000841088 7001_ $$aRobbins, Garry.
000841088 852__ $$bebk
000841088 85640 $$3ProQuest Ebook Central Academic Complete$$uhttps://univsouthin.idm.oclc.org/login?url=https://ebookcentral.proquest.com/lib/usiricelib-ebooks/detail.action?docID=1057451$$zOnline Access
000841088 909CO $$ooai:library.usi.edu:841088$$pGLOBAL_SET
000841088 980__ $$aEBOOK
000841088 980__ $$aBIB
000841088 982__ $$aEbook
000841088 983__ $$aOnline