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List of contents
Introduction; 1. Network analysis today; Part I. Thinking Structurally: 2. What is social structure?; 3. What is a social network?; 4. How are social network data collected?; 5. How are social network data visualized?; Part II. Seeing Structure: 6. Structuration and ego-centric networks; 7. Sociality and elementary forms of structure; 8. Cohesion and groups; 9. Hierarchy and centrality; 10. Positions and roles; 11. Affiliations and dualities; 12. Networks and culture; Part III. Making Structural Predictions: 13. Models for networks; 14. Models for network diffusion; 15. Models for social influence; Conclusion: 16. Network analysis tomorrow.
About the author
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.
