Models for Social Networks With Statistical Applications
- Suraj Bandyopadhyay - Indian Statistical Institute, India
- A R. Rao - Indian Statistical Institute, India
- Bikas K. Sinha - Indian Statistical Institute, India
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.
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
Too advanced. May consider for a future advanced research methods course.
This is a great text that I hope to use in a more advanced research methods class.
I found this textbook to be good in terms of exposing the graph theoretic background required of this research domain but the book is lagging significantly in terms of application scope. There is a need to discuss a broader spectrum of applications of SNA.
This book, while well written, is quite heavy on mathematical formulas and not suitable for this class. The book uses well thought out case studies and presents a unique perspective on social networks.
It is a wonderful comprehensive book, but far beyond the level of my students' statistical abilities, besides, the focus was different from what I expected. We focus more on interpretation of statistical analysis than designing at this level. Thanks for the opportunity to review it.
Sample Materials & Chapters
Chapter 1 - Introduction to Social Network Analysis
Chapter 3 - Graph-Theoretic and Statistical Models