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Library of Congress Cataloging-in-Publication Data
Names: Knoke, David, author. | Yang, Song, author.
Title: Social network analysis / David Knoke, University of Minnesota, Song Yang, University of Arkansas.
Description: Third edition. | Thousand Oaks, California: SAGE, [2020] |
Series: Quantitative applications in the social sciences; 154 | Includes bibliographical references and index.
Identifiers: LCCN 2019031097 | ISBN 9781506389318 (paperback) | ISBN 9781506389295 (epub) | ISBN 9781506389301 (epub) | ISBN 9781506389325 (web pdf)
Subjects: LCSH: Social networks.
Classification: LCC HM741 .K66 2020 | DDC 302.3—dc23
LC record available at https://lccn.loc.gov/2019031097
Printed in the United States of America
This book is printed on acid-free paper.
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Copy Editor: Celia McCoy
Typesetter: Hurix Digital
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Cover Designer: Candice Harman
Marketing Manager: Shari Countryman
Series Editor’s Introduction
Much has happened since the publication of Social Network Analysis, 2nd edition. Perhaps most importantly from the standpoint of its content, “social network” has entered the modern lexicon. Facebook and YouTube started in the mid-2000s, quickly followed by Twitter, Snapchat, WhatsApp, and others. Social media applications have exploded. The percentage of U.S. adults using at least one social media site increased from 5% in 2005 to 25% in 2008, to 50% in 2011, and is now nearly 75% according to Pew Research Center estimates. Of course, social networks are not new. They have formed the organizational backbone of social life for many millennia and have been a focus of social science research for almost a century. What is new is broad public interest in social networks, including how they can be manipulated, for good or ill. Also new is the creation and accumulation of massive online datasets reflecting and recording participation in social media. These trends have inspired David Knoke and Song Yang to issue a new edition of their classic text.
As with the earlier editions, Social Network Analysis, 3rd Edition, provides a concise introduction to the concepts and tools of social network analysis. The authors are highly regarded technical experts, and the field itself can be quite complicated, but, as was the case with the earlier editions, this “little green cover” is readily accessible. Professors Knoke and Yang convey key material while at the same time minimize technical complexities. The examples are simple—sets of five or six entities such as individuals, positions in a hierarchy, political offices, and nation-states. The set or sets of relations between them include friendship, communication, supervision, donations, and trade.
As with earlier editions, Social Network Analysis, 3rd Edition, would serve well as a course supplement at the undergraduate or graduate level. The authors have gone to great lengths to keep the math simple in all but the final chapter of the monograph. The volume is organized in a clear and straightforward manner. After a brief introduction in the first chapter, which situates the study of social networks in a broader context, the second chapter takes up “network fundamentals,” defines central concepts, and demonstrates multiple perspectives on how networks can be viewed and studied. Chapter 3 addresses social network data collection, specifically, how the choices made at the design phase such as how to define membership and where to set the boundary, how to sample network entities, and which relations to measure affect subsequent analysis and inference. This chapter also discusses missing data and data quality more generally. In Chapter 4, Professors Knoke and Yang introduce basic methods for analyzing networks, presenting measures of nodes (e.g., degree centrality), dyads (e.g., reachability), subgroups (e.g., cliques), and whole networks (e.g., centralization). They describe and explain strict and more relaxed forms of structural equivalence at the end of the chapter. Level of difficulty increases in Chapter 5. Matrix algebra is needed for parts of this chapter, whereas basic algebra is all that is needed for Chapters 1 through 4. Chapter 5 introduces readers to advanced analytic methods such as clustering, multidimensional scaling, blockmodeling, community detection, and exponential random graph models (ERGMs), preparing them to read the technical literature on these topics.
In comparison with earlier editions, Social Network Analysis, 3rd Edition, reflects developments and changes in practice over the past decade. To begin with, Professors Knoke and Yang update the specific language used by network researchers (e.g., whole networks rather than complete networks). In addition, they expand coverage of some topics. For example, whereas the earlier edition presented affiliation models in terms of bipartite models alone, the third edition provides a more general discussion, covering tripartite as well as bipartite models. The authors also describe important recent developments in network analysis, especially in the fifth chapter. ERGMs are a prime example. Analysts interested in statistically modeling network ties as an outcome need to account for clustering and endogeneity. When the second edition was published, P* models were the recommended approach for this, but they have been replaced by ERGMs since then. Finally, throughout the volume, Professors Knoke and Yang comment on the challenges and opportunities offered by Internet and social media data.
Social Network Analysis is one of the most popular “little green books” in the Quantitative Applications in the Social Sciences series. It draws on the authors’ years of experience to provide an initial entrée into a highly complex area of study, laying a firm foundation on which readers at all levels can continue to build. With the publication of the third edition, if anything, its popularity will increase.
—Barbara Entwisle
Series Editor
About the Authors
