Exploratory Social Network Analysis With Pajek

De Nooy, Wouter/ Mrvar, Andrej/ Batagelj, Vladimir

ISBN 10: 1108462278 / ISBN 13: 9781108462273

Editore: Cambridge Univ Pr, 2018

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3rd revised expanded edition. 450 pages. 9.00x6.00x1.00 inches. In Stock.

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Dati bibliografici

• Titolo: Exploratory Social Network Analysis With Pajek
• Editore: Cambridge Univ Pr
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 1108462278
• ISBN 13: 9781108462273
• Rilegatura: Paperback
• Condizione: Brand New

Descrizione articolo

Riassunto

Presents an analysis and visualization of social networks integrating theory, applications, and professional software for performing network analysis (Pajek).

Informazioni sugli autori

Wouter de Nooy is Associate Professor in the Department of Communication Science at the Universiteit van Amsterdam, the Netherlands. He is a member of the Amsterdam School of Communication Research ASCoR. His research interests include dynamic models for social networks and their application in culture and politics.

Andrej Mrvar is Professor of Social Science Informatics at the Faculty of Social Sciences, University of Ljubljana, Slovenia. He started developing programs Pajek, PajekXXL and Pajek3XL as part of his Ph.D. dissertation at the Faculty of Computer and Information Science in 1996. Since then Pajek has been in constant development.

Vladimir Batagelj is Professor Emeritus of the University of Ljubljana, Slovenia. He is also a member of Inštitut za matematiko, fiziko in mehaniko (IMFM), Ljubljana, and of the Univerza na Primorskem, Inštitut Andrej Marušič (UP, IAM), Koper. His co-authored book Generalized Blockmodeling (Cambridge, 2005) was awarded the 2007 Harrison White Outstanding Book Award by the Mathematical Sociology Section of American Sociological Association (ASA). From International Network for Social Network Analysis (INSNA) he was awarded the Georg Simmel Award (2007) and the Richards Software Award for the program Pajek (2013). In 2014 he co-authored a book entitled Understanding Large Temporal Networks and Spatial Networks.