Generalized Blockmodeling

Patrick Doreian is a Professor of Sociology and Statistics at the University of Pittsburgh and is chair of the Department of Sociology. He has edited the Journal of Mathematical Sociology since 1982 and has been a member of the editorial board for Social Networks since 2003. He was a Centennial Professor at The London School of Economics during 2002. He has been a Visiting Professor at the University of California-Irvine and the University of Ljubljana. His interests include social networks, mathematical sociology, interorganizational networks, environmental sociology and social movements. Vladimir Batagelj is a Professor of Discrete and Computational Mathematics at the University of Ljubljana and is chair of the Department of Theoretical Computer Science at IMFM, Ljubljana. He is a member of editorial boards of Informatica and Journal of Social Structure. He was visiting professor at University of Pittsburgh in 1990 to 1991 and at University of Konstanz (Germany) in 2002. His main research interests are in graph theory, algorithms on graphs and networks, combinatorial optimization, data analysis and applications of information technology in education. He is coauthor (with Andrej Mrvar) of Pajek - a program for analysis and visualization of large networks. Anu∫ka Ferligoj is a Professor of Statistics at the University of Ljubljana and is dean of the Faculty of Social Sciences. She is editor of the series Metodoloski zvezki since 1987 and is a member of the editorial boards of the Journal of Mathematical Sociology, Journal of Classification, Social Networks, and Statistics in Transition. She was a Fulbright scholar in 1990 and Visiting Professor at the University of Pittsburgh. She was awarded the title of Ambassador of Science of the Republic of Slovenia in 1997. Her interests include multivariate analysis (constrained and multicriteria clustering), social networks (measurement quality and blockmodeling), and survey methodology (reliability and validity of measurement).

'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

• 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.