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Exploratory Social Network Analysis with Pajek : Revised and Expanded Edition for Updated Software - Expanded edition

By: (Author) Andrej Mrvar , (Author) Vladimir Batagelj , (Author) Wouter De Nooy

Manufacture on Demand

Ksh 7,800.00

Format: Paperback or Softback

ISBN-10: 1108462278

ISBN-13: 9781108462273

Edition: Expanded edition

Series: Structural Analysis in the Social Sciences

Publisher: Cambridge University Press

Imprint: Cambridge University Press

Country of Manufacture: US

Country of Publication: GB

Publication Date: Jul 19th, 2018

Print length: 484 Pages

Weight: 664 grams

Dimensions (height x width x thickness): 15.30 x 22.80 x 2.70 cms

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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.
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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