Generalized blockmodeling / Patrick Doreian, Vladimir Batagelj, Anuška Ferligoj.

Format:

Book

Author/Creator:

Doreian, Patrick, author.

Batagelj, Vladimir, 1948- author.

Ferligoj, Anuška, author.

Series:

Structural analysis in the social sciences ; 25.

Structural analysis in the social sciences ; 25

Language:

English

Subjects (All):

Social networks--Mathematical models.

Social networks.

Sociometry.

Physical Description:

1 online resource (xv, 384 pages) : digital, PDF file(s).

Place of Publication:

Cambridge : Cambridge University Press, 2005.

Language Note:

English

Summary:

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.

Contents:

CONTENTS; PREFACE; 1 SOCIAL NETWORKS AND BLOCKMODELS; 1.1 AN INTUITIVE STATEMENT OF NETWORK IDEAS; 1.1.1 Fundamental types of social relations; 1.1.2 Types of relational data arrays; 1.2 BLOCKS AS PARTS OF NETWORKS; 1.2.1 Blocks; 1.3 SOME BLOCK TYPES; 1.4 SPECIFYING BLOCKMODELS; 1.4.1 Parent-child role systems; 1.4.2 Organizational hierarchies; 1.4.3 Systems of ranked clusters; 1.4.4 Baboon grooming networks; 1.5 CONVENTIONAL BLOCKMODELING; 1.5.1 Equivalence and blockmodeling; 1.6 GENERALIZED BLOCKMODELING; 1.7 AN OUTLINE MAP OF THE TOPICS CONSIDERED; 2 NETWORK DATA SETS

2.1 CLASSIC DATA SETS2.1.1 Sampson monastery data; 2.1.2 Bank wiring room data; 2.1.3 Newcomb fraternity data; 2.2 NEWER DATA SETS; 2.2.1 Little league baseball teams; 2.2.2 Political actor network; 2.2.3 Student government data; 2.2.4 Kansas search and rescue network; 2.2.5 A Bales-type group dynamics network; 2.2.6 Ragusan families marriage networks; 2.2.7 Two baboon grooming networks; 2.3 DATA SET PROPERTIES; 2.4 SOME ADDITIONAL REMARKS CONCERNING DATA; 3 MATHEMATICAL PRELUDE; 3.1 BASIC SET THEORY; 3.2 RELATIONS; 3.2.1 Operations with binary relations; 3.2.2 Comparing relations

3.2.3 Special operations3.3 FUNCTIONS; 3.3.1 Products of functions; 3.3.2 Relational homomorphisms; 3.4 BASIC ALGEBRA; 3.5 TRANSITIONS TO CHAPTERS 4 AND 9; 4 RELATIONS AND GRAPHS FOR NETWORK ANALYSIS; 4.1 GRAPHS; 4.1.1 Examples of graphs; 4.1.2 Traveling on a graph; 4.1.3 Graph coloring; 4.2 TYPES OF BINARY RELATIONS; 4.2.1 Properties of relations; 4.2.2 Closures; 4.2.3 Computing the transitive closure; 4.2.4 Special elements; 4.2.5 Tournaments; 4.3 PARTITIONS AND EQUIVALENCE RELATIONS; 4.4 ACYCLIC RELATIONS; 4.4.1 Levels; 4.5 ORDERS; 4.5.1 Factorization; 4.5.2 Hasse diagram; 4.5.3 Numberings

4.6 NETWORKS4.7 CENTRALITY IN NETWORKS; 4.7.1 Algorithmic aspects; 4.8 SUMMARY AND TRANSITION; 5 CLUSTERING APPROACHES; 5.1 AN INTRODUCTION TO CLUSTER ANALYTIC IDEAS; 5.2 USUAL CLUSTERING PROBLEMS; 5.2.1 An example; 5.2.2 The usual steps of solving clustering problems; 5.3 (DIS)SIMILARITIES; 5.3.1 (Dis)similarity measures for numerical data; 5.3.2 (Dis)similarity measures for binary data; 5.4 CLUSTERING ALGORITHMS; 5.4.1 The hierarchical approach; 5.4.2 The leader algorithm; 5.4.3 The relocation algorithms; 5.5 CONSTRAINED CLUSTERING; 5.5.1 The constrained clustering problem

5.5.2 Solving constrained clustering problems5.5.3 The structure enforcement coefficient; 5.5.4 An empirical example; 5.6 MULTICRITERIA CLUSTERING; 5.6.1 A multicriteria clustering problem; 5.6.2 Solving discrete multicriteria optimization problems; 5.6.3 Direct multicriteria clustering algorithms; 5.6.4 An example; 5.7 TRANSITION TO BLOCKMODELING; 6 AN OPTIMIZATIONAL APPROACH TO CONVENTIONAL BLOCKMODELING; 6.1 CONVENTIONAL BLOCKMODELING; 6.1.1 Definitions of equivalences; 6.1.2 Equivalence and k-partite graphs; 6.1.3 Establishing conventional blockmodels

6.1.4 The indirect blockmodeling approach

Notes:

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Includes bibliographical references (p. [363]-374) and indexes.

ISBN:

1-107-14011-0

1-280-43718-9

0-511-29804-8

0-511-08088-3

0-511-17091-2

0-511-19646-6

0-511-58417-2

OCLC:

770008728