Wouter De Nooy

Exploratory Social Network Analysis with Pajek (eBook, PDF)

Revised and Expanded Edition for Updated Software

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 19. Juli 2018
• Englisch
• ISBN-13: 9781108591195
• Artikelnr.: 53463474

Autorenporträt

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.

Inhaltsangabe

Part I. Fundamentals: 1. Looking for social structure
2. Attributes and relations
Part II. Cohesion: 3. Cohesive subgroups
4. Sentiments and friendship
5. Affiliations
Part III. Brokerage: 6. Center and periphery
7. Brokers and bridges
8. Diffusion
Part IV. Ranking: 9. Prestige
10. Ranking
11. Genealogies and citations
Part V. Roles: 12. Blockmodels
13. Random graph models.