Written by a sociologist, a graph theorist, and a statistician, this title provides social network analysts and students with a solid statistical foundation from which to analyze network data. Clearly demonstrates how graph-theoretic and statistical techniques can be employed to study some important parameters of global social networks. The authors uses real life village-level social networks to illustrate the practicalities, potentials, and constraints of social network analysis (‘SNA’). They also offer relevant sampling and inferential aspects of the techniques while dealing with potentially large networks.
Intended Audience
This supplemental text is ideal for a variety of graduate and doctoral level courses in social network analysis in the social, behavioral, and health sciences
Tabela de Conteúdo
PREFACE
1. Introduction to Social Network Analysis
2. Introduction to Digraphs
3. Graph-Theoretic and Statistical Models
4. Validation of Statistical Models
5. Graph-theoretic and Statistical Measures and Methods
6. Graph-theoretic Case Studies
7. Sampling and Inference in a Social Network
Sobre o autor
Bikas K Sinha (Ph.D., Statistics, Calcutta University) is a Professor of Statistics in the Division of Theoretical Statistics and Mathematics of the Indian Statistical Institute, Kolkata. He was a recipient of PCMahalanobis Medal in 1980. He has served as an Expert on Mission in Survey Methodology for the United Nations and has also served as a Consultant for US Environmental Protection Agency [EPA]. He has traveled extensively and visited a host of universities in USA, Canada, Germany, Finland, Poland and other countries as a Visiting Faculty / Research Collaborator. He has more than 110 research articles in refereed journals, one graduate-level text book [Wiley, NY] and two Research Monographs [Springer-Verlag Lecture Notes Series in Statistics Publications]. He is an elected member of the International Statistical Institute. His range of expertise includes survey theory and methods, design of experiments, statistical modeling and statistical inference.
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