Exponential random graph models for social networks : theories, methods, and applications / editors, Dean Lusher, Johan Koskinen, Garry Robbins.
"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 reciproc...
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| Format: | Electronic eBook |
| Language: | English |
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Cambridge University Press,
2013.
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| Series: | Structural analysis in the social sciences ;
35. |
| Subjects: |
MARC
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| 245 | 0 | 0 | |a Exponential random graph models for social networks : |b theories, methods, and applications / |c editors, Dean Lusher, Johan Koskinen, Garry Robbins. |
| 260 | |a Cambridge : |b Cambridge University Press, |c 2013. | ||
| 300 | |a 1 online resource (xxii, 336 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Structural analysis in the social sciences ; |v 35 | |
| 504 | |a Includes bibliographical references and index. | ||
| 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"-- |c Provided by publisher. | ||
| 505 | 0 | 0 | |g 1. |t Introduction / |r Dean Lusher, Johan Koskinen and Garry Robins -- |g 2, |t What are exponential random graph models / |r Garry Robins and Dean Lusher -- |g 3, |t The formation of social network structure / |r Dean Lusher and Garry Robins -- |g 4. |t Simplified account of exponential random graph model as a statistical model / |r Garry Robins and Dean Lusher -- |g 5, |t Example of exponential random graph model analysis / |r Dean Lusher and Garry Robins -- |g 6. |t Exponential random graph model fundamentals / |r Johan Koskinene and Galina Daragonova -- |g 7. |t Dependence graphs and sufficient statistics / |r Johan Koskinen and Galina Daragonova -- |g 8. |t Social selection, dyadic covariates, and geospatial effects / |r Garry Robins and Galina Daragonova -- |g 9. |t Autologistic actor attribute models / |r Galina Daragonova and Garry Robins -- |g 10. |t Exponential random graph model extensions: models for multiple networks and bipartite networks / |r Peng Wang -- |g 11. |t 10. Longitudinal models / |r Tom Snijders and Johan Koskinen -- |t g12. |t Simulation, estimation, and goodness of fit / |r Johan Koskinen and Tom Snijders -- |g 13. |t Illustrations: simulation, estimation and goodness of fit / |r Garry Robins and Dean Lusher -- |g 14. |t Personal attitudes, perceived attitudes, and social structures: a social selection model / |r Dean Lusher and Garry Robins -- |g 15. |t How to close a hole: exploring alternative closure mechanisms in interorganizational networks / |r Alessandro Lomi and Francesca Pallotti -- |g 16. |t Interdependencies between working relations: multivariate ERGMs for advice and satisfaction / |r Yu Zhao and Olaf Rank -- |g 16. |t Brain, brawn, or optimism?: structure and correlates of emergent military leadership / |r Yuval Kalish and Gil Luria -- |g 18. |t Autologistic actor attribute model analysis of unemployment: dual importance of who you know and where you live / |r Galina Daragonova and Pip Pattison -- |g 19. |t Longitudinal changes in face-to-face and text message-mediated friendship networks / |r Tasuku Igarashi -- |g 20. |t Differential impact of directors' social and financial capital on corporate interlock formation / |r Nicholas Harrigan and Matthew Bond -- |g 21. |t Comparing networks: a structural correspondence between behavioral and recall networks / |r Eric Quintane -- |g 22. |t Modeling social networks: next steps / |r Pip Pattison and Tom Snijders. |
| 650 | 0 | |a Social networks |x Mathematical models. | |
| 650 | 0 | |a Social networks |x Research |x Graphic methods. | |
| 650 | 7 | |a Social networks |x Mathematical models. |2 fast |0 (OCoLC)fst01122685 | |
| 700 | 1 | |a Lusher, Dean. | |
| 700 | 1 | |a Koskinen, Johan. | |
| 700 | 1 | |a Robbins, Garry. | |
| 776 | 0 | 8 | |i Print version: |z 9780521193566 |z 0521193567 |w (DLC) 2012021034 |
| 830 | 0 | |a Structural analysis in the social sciences ; |v 35. | |
| 856 | 4 | 0 | |u https://colorado.idm.oclc.org/login?url=https://doi.org/10.1017/CBO9780511894701 |z Full Text (via Cambridge) |
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