Network Analysis : Integrating Social Network Theory, Method, and Application With R

The size and availability of network information has exploded over the last decade. Social scientists now share the stage of network analysis with computer scientists, physicists, and statisticians. While a number of introductions to network analysis are now available, most focus on theory, methods, or application alone. This book integrates all three. Network Analysis is an introduction to both the why and how of Social Network Analysis (SNA). It presents a broad theoretical overview rooted in social scientific approaches and guides users in how network analysis can answer core theoretical questions. It provides a comprehensive overview of descriptive and analytical approaches, including practical tutorials in R with sample data sets. Using an integrated approach, this book aims to quickly bring novice network researchers up to speed while avoiding common programming and analysis mistakes so that they might gain insight into the fundamental theories, key concepts, and methodological application of SNA.

Craig M. Rawlings is Associate Professor of Sociology at Duke University where he is affiliated with the Duke Network Analysis Center. His work focuses on the connections between social structures and culture, including belief systems, knowledge, meaning-making processes, and attitude change. His publications have appeared in the American Journal of Sociology, American Sociological Review, Social Forces, Sociological Science, and Poetics.

Jeffrey A. Smith is Associate Professor of Sociology at the University of Nebraska–Lincoln. He has done methodological work on network sampling and missing data, as well as more substantive work on network processes, drug use, and health outcomes. His work has been published in the American Sociological Review, Sociological Methodology, Social Networks, and other venues.

James Moody is Professor of Sociology at Duke University and focuses on the network foundations of social cohesion and diffusion, using networks to help understand topics including racial segregation, disease spread, and the development of scientific disciplines. He has won the Freeman Award for contributions to network analysis and a James S. McDonnel Foundation Complexity Scholars award.

Daniel A. McFarland is Professor of Education and (by courtesy) Sociology and Organizational Behavior at Stanford University, where he founded Stanford's Center for Computational Social Science. His past work studied social network dynamics of communication, relationships, affiliations, and knowledge structures in educational contexts. His current work integrates social network analysis and natural language processing to study the development of scientific knowledge.