Generalized Blockmodeling: 25 (Structural Analysis in the Social Sciences, Series Number 25)
Cambridge University Press
Edition: Illustrated, 2/3/2005
EAN 9780521840859, ISBN10: 0521840856
Hardcover, 402 pages, 22.9 x 15.2 x 2.7 cm
Language: English
This book provides an integrated treatment of blockmodeling, the most frequently used technique in social network analysis. It secures its mathematical foundations and then generalizes blockmodeling for the analysis of many types of network structures. Examples are used throughout the text and include small group structures, little league baseball teams, intra-organizational networks, inter-organizational networks, baboon grooming networks, marriage ties of noble families, trust networks, signed networks, Supreme Court decisions, journal citation networks, and alliance networks. Also provided is an integrated treatment of algebraic and graph theoretic concepts for network analysis and a broad introduction to cluster analysis. These formal ideas are the foundations for the authors' proposal for direct optimizational approaches to blockmodeling which yield blockmodels that best fit the data, a measure of fit that is integral to the establishment of blockmodels, and creates the potential for many generalizations and a deductive use of blockmodeling.
Preface
1. Social networks and blockmodels
2. Network data sets
3. Mathematical prelude
4. Relations and graphs for network analysis
5. Clustering approaches
6. An optimizational approach to conventional blockmodeling
7. Foundations for generalized blockmodeling
8. Blockmodeling two-mode network data
9. Semirings and lattices
10. Balance theory and blockmodeling signed networks
11. Symmetric-acyclic blockmodels
12. Extending generalized blockmodeling
Bibliography
Author index
Subject index.
'This is a clearly presented and insightful book that provides an excellent mix of mathematical rigor and practical application. I would unhesitatingly recommend the book to anyone interested in social network analysis or discrete clustering methods.' Journal of Classification