An extensively revised and expanded third edition of the successful textbook on analysis and visualization of social networks integrating theory, applications, and professional software for performing network analysis (Pajek). The main structural concepts and their applications in social research are introduced with exercises. Pajek software and datasets are available, so readers can learn network analysis through application and case studies. In the end readers will have the knowledge, skills, and tools to apply social network analysis across different disciplines. A fundamental redesign of the menu structure and the capability to analyze much larger networks required a new edition. This edition presents several new operations including community detection, generalized main paths searches, new network indices, advanced visualization approaches, and instructions for installing Pajek under MacOSX. This third edition is up-to-date with Pajek version 5 and it introduces PajekXXL for very large networks and Pajek3XL for huge networks.
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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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Paperback. Etat : new. Paperback. An extensively revised and expanded third edition of the successful textbook on analysis and visualization of social networks integrating theory, applications, and professional software for performing network analysis (Pajek). The main structural concepts and their applications in social research are introduced with exercises. Pajek software and datasets are available, so readers can learn network analysis through application and case studies. In the end readers will have the knowledge, skills, and tools to apply social network analysis across different disciplines. A fundamental redesign of the menu structure and the capability to analyze much larger networks required a new edition. This edition presents several new operations including community detection, generalized main paths searches, new network indices, advanced visualization approaches, and instructions for installing Pajek under MacOSX. This third edition is up-to-date with Pajek version 5 and it introduces PajekXXL for very large networks and Pajek3XL for huge networks. The textbook on analysis and visualization of social networks that integrates theory, applications, and professional software for performing network analysis. Pajek software and datasets for all examples are freely available, so the reader can learn network analysis by doing it. Each chapter offers case studies for practicing network analysis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781108462273
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