Inbunden, Engelska, 2012
Exponential Random Graph Models for Social Networks
Theory, Methods, and Applications
Av Dean Lusher, Johan Koskinen, Garry Robins
Del 35 i serien Structural Analysis in the Social Sciences
1277 kr
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Beskrivning
Produktinformation
- Utgivningsdatum: 2012-11-19
- Mått: 158 x 235 x 23 mm
- Vikt: 600 g
- Format: Inbunden
- Språk: Engelska
- Serie: Structural Analysis in the Social Sciences
- Antal sidor: 352
- Förlag: Cambridge University Press
- ISBN: 9780521193566
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Dr Dean Lusher is Lecturer in Sociology at Swinburne University of Technology. He works closely with leading methodologists to develop an intuitive understanding of exponential graph models, how they link to broader network theory, and how to fit them to real-life data. His research applications are directed at issues of social norms and social hierarchies. Dr Johan Koskinen is Lecturer in Social Sciences at the University of Manchester. He is a statistician working with statistical modeling and inference. Focusing on social network data, Dr Koskinen deals with generative models for different types of structures, such as longitudinal network data, networks nested in multilevel structures, and multilevel networks classified by affiliations. Garry Robins is Professor in the School of Psychological Sciences at the University of Melbourne. Robins is a mathematical psychologist whose research deals with quantitative and statistical models for social and relational systems. His research has won international awards from the Psychometric Society, the American Psychological Association, and the International Network for Social Network Analysis.
Recensioner i media
'I've been waiting impatiently for this book and I was definitely not disappointed. Finally we have a sourcebook on ERGMs that is both comprehensive and comprehensible. Most of the chapters are written for quantitative researchers who are not statisticians. Many illustrative empirical applications are worked through. Software packages are discussed. For the researcher who is intrigued by the possibility of analyzing network data with an ERGM, or who is already trying to do so, this is an indispensable resource.' Peter Carrington, University of Waterloo
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