Social network analysis: methods and applications
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2007
|
| Ausgabe: | 1. publ., repr. |
| Schriftenreihe: | Structural analysis in the social sciences
8 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XXXI, 825 S. graph. Darst. |
| ISBN: | 9780521382694 9780521387071 |
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MARC
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| 100 | 1 | |a Wasserman, Stanley |e Verfasser |0 (DE-588)13036018X |4 aut | |
| 245 | 1 | 0 | |a Social network analysis |b methods and applications |c Stanley Wasserman ; Katherine Faust |
| 250 | |a 1. publ., repr. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2007 | |
| 300 | |a XXXI, 825 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Structural analysis in the social sciences |v 8 | |
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 650 | 4 | |a Analiz - 880-05 - metodi | |
| 650 | 4 | |a Izsledvanii͡a - 880-07 - metodologii͡a | |
| 650 | 4 | |a Sot͡sialni nauki - 880-06 - izsledvanii͡a - metodologii͡a | |
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| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Faust, Katherine |e Verfasser |4 aut | |
| 830 | 0 | |a Structural analysis in the social sciences |v 8 |w (DE-604)BV002814947 |9 8 | |
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| 856 | 4 | 2 | |m Digitalisierung UB Passau |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016140974&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |3 Klappentext |
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Datensatz im Suchindex
| _version_ | 1804137170540167168 |
|---|---|
| adam_text | Contents
List of Tables page
xxi
List of Illustrations
xxiv
Preface
xxix
Part I: Networks, Relations, and Structure
1
1
Social Network Analysis in the Social and Behavioral Sciences
3
1.1
The Social Networks Perspective
4
1.2
Historical and Theoretical Foundations
10
1.2.1
Empirical Motivations
11
1.2.2
Theoretical Motivations
13
1.2.3
Mathematical Motivations
15
1.2.4
In Summary
16
1.3
Fundamental Concepts in Network Analysis
17
1.4
Distinctive Features
21
1.5
Organization of the Book and How to Read It
22
1.5.1
Complexity
23
1.5.2
Descriptive and Statistical Methods
23
1.5.3
Theory Driven Methods
24
1.5.4
Chronology
24
1.5.5
Levels of Analysis
25
1.5.6
Chapter Prerequisites
26
1.6
Summary
27
2
Social Network Data
28
2.1
Introduction: What Are Network Data?
28
2.1.1
Structural and Composition Variables
29
2.1.2
Modes
29
2.1.3 Affiliation
Variables
ЗО
2.2
Boundary Specification and Sampling
30
2.2.1
What Is Your Population?
31
2.2.2
Sampling
33
2.3
Types of Networks
35
2.3.1
One-Mode Networks
36
2.3.2
Two-Mode Networks
39
2.3.3
Ego-centered and Special Dyadic Networks
41
2.4
Network Data, Measurement and Collection
43
2.4.1
Measurement
43
2.4.2
Collection
45
2.4.3
Longitudinal Data Collection
55
2.4.4
Measurement Validity, Reliability, Accuracy, Error
56
2.5
Data Sets Found in These Pages
59
2.5.1
Krackhardt s High-tech Managers
60
2.5.2
Padgett s Florentine Families
61
2.5.3
Freeman s
EIES
Network
62
2.5.4
Countries Trade Data
64
2.5.5
Galaskiewicz s CEOs and Clubs Network
65
2.5.6
Other Data
66
Part II: Mathematical Representations of Social Networks
67
3
Notation for Social Network Data
69
3.1
Graph Theoretic Notation
71
3.1.1
A Single Relation
71
3.1.2
OMultiple Relations
73
3.1.3
Summary
75
3.2
Sociometrie
Notation
77
3.2.1
Single Relation
79
3.2.2
Multiple Relations
81
3.2.3
Summary
83
3.3
OAlgebraic Notation
84
3.4
OTwo Sets of Actors
85
3.4.1
(g)Different Types of Pairs
86
3.4.2
OSociometric Notation
87
3.5
Putting It All Together
89
Graphs and Matrices
92
4.1
Why Graphs?
93
4.2
Graphs
94
4.2.1
Subgraphs, Dyads, and Triads
97
4.2.2
Nodal Degree
100
4.2.3
Density of Graphs and Subgraphs
101
4.2.4
Example: Padgett s Florentine Families
103
4.2.5
Walks, Trails, and Paths
105
4.2.6
Connected Graphs and Components
109
4.2.7
Geodesies, Distance, and Diameter
110
4.2.8
Connectivity of Graphs
112
4.2.9
Isomorphic Graphs and Subgraphs
117
4.2.10
OSpecial Kinds of Graphs
119
4.3
Directed Graphs
121
4.3.1
Subgraphs
-
Dyads
124
4.3.2
Nodal Indegree and Outdegree
125
4.3.3
Density of a Directed Graph
129
4.3.4
An Example
129
4.3.5
Directed Walks, Paths, Semipaths
129
4.3.6
Reachability and Connectivity in Digraphs
132
4.3.7
Geodesies, Distance and Diameter
134
4.3.8
OSpecial Kinds of Directed Graphs
134
4.3.9
Summary
136
4.4
Signed Graphs and Signed Directed Graphs
136
4.4.1
Signed Graph
137
4.4.2
Signed Directed Graphs
138
4.5
Valued Graphs and Valued Directed Graphs
140
4.5.1
Nodes and Dyads
142
4.5.2
Density in a Valued Graph
143
4.5.3
OPaths in Valued Graphs
143
4.6
Multigraphs
145
4.7
0Hypergraphs
146
4.8
Relations
148
4.8.1
Definition
148
4.8.2
Properties of Relations
149
4.9
Matrices
150
4.9.1
Matrices for Graphs
150
4.9.2
Matrices for Digraphs
152
4.9.3
Matrices for Valued Graphs
153
4.9.4
Matrices for Two-Mode Networks
154
4.9.5
OMatrices for Hypergraphs
4.9.6
Basic Matrix Operations
4.9.7
Computing Simple Network Properties
4.9.8
Summary
4.10
Properties
4.10.1
Reflexivity ;
4.10.2
Symmetry
4.10.3
Transitivity
4.11
Summary
Part III: Structural and Locational Properties
5
Centrality and Prestige
5.1
Prominence: Centrality and Prestige
5.1.1
Actor Centrality
5.1.2
Actor Prestige
5.1.3
Group Centralization and Group Prestige
5.2
Nondirectional Relations
5.2.1
Degree Centrality
5.2.2
Closeness Centrality
5.2.3
Betweenness Centrality
5.2.4
(^Information Centrality
5.3
Directional Relations
5.3.1
Centrality
5.3.2
Prestige
5.3.3
A Different Example
5.4
Comparisons and Extensions
6
Structural Balance and Transitivity
6.1
Structural Balance
6.1.1
Signed Nondirectional Relations
6.1.2
Signed Directional Relations
6.1.3
OChecking for Balance
6.1.4
An Index for Balance
6.1.5
Summary
6.2
Clusterability
6.2.1
The Clustering Theorems
6.2.2
Summary
6.3
Generalizations of Clusterability
6.3.1
Empirical Evidence
239
6.3.2
ORanked Clusterability
240
6.3.3
Summary
242
6.4
Transitivity
243
6.5
Conclusion
247
Cohesive Subgroups
249
7.1
Background
250
7.1.1
Social Group and Subgroup
250
7.1.2
Notation
252
7.2
Subgroups Based on Complete Mutuality
253
7.2.1
Definition of a Clique
254
7.2.2
An Example
254
7.2.3
Considerations
256
7.3
Reachability and Diameter
257
7.3.1
«-cliques
258
7.3.2
An Example
259
7.3.3
Considerations
260
7.3.4
«-clans and
и
-clubs
260
7.3.5
Summary
262
7.4
Subgroups Based on Nodal Degree
263
7.4.1
fc-plexes
265
7.4.2
fe-cores
266
7.5
Comparing Within to Outside Subgroup Ties
267
7.5.1
LS Sets
268
7.5.2
Lambda Sets
269
7.6
Measures of Subgroup Cohesion
270
7.7
Directional Relations
273
7.7.1
Cliques Based on Reciprocated Ties
273
7.7.2
Connectivity in Directional Relations
274
7.7.3
«-cliques in Directional Relations
275
7.8
Valued Relations
277
7.8.1
Cliques, «-cliques, and fe-plexes
278
7.8.2
Other Approaches for Valued Relations
282
7.9
Interpretation of Cohesive Subgroups
283
7.10
Other Approaches
284
7.10.1
Matrix Permutation Approaches
284
7.10.2
Multidimensional Scaling
287
7.10.3
OFactor Analysis
290
7.11
Summary
290
8
Affiliations and Overlapping Subgroups
8.1
Affiliation Networks
8.2
Background
8.2.1
Theory
8.2.2
Concepts
8.2.3
Applications and Rationale
8.3
Representing Affiliation Networks
8.3.1
The Affiliation Network Matrix
8.3.2
Bipartite Graph
8.3.3
Hypergraph
8.3.4
OSimplices and Simplicial Complexes
8.3.5
Summary
8.3.6
An example: Galaskiewicz s CEOs and Clubs
8.4
One-mode Networks
8.4.1
Definition
8.4.2
Examples
8.5
Properties of Affiliation Networks
8.5.1
Properties of Actors and Events
8.5.2
Properties of One-mode Networks
8.5.3
Taking Account of Subgroup Size
8.5.4
Interpretation
8.6
(^Analysis of Actors and Events
8.6.1
(g)Galois Lattices
8.6.2
(^Correspondence Analysis
8.7
Summary
Part IV: Roles and Positions
345
9
Structural Equivalence
347
9.1
Background
348
9.1.1
Social Roles and Positions
348
9.1.2
An Overview of Positional and Role Analysis
351
9.1.3
A Brief History
354
9.2
Definition of Structural Equivalence
356
9.2.1
Definition
356
9.2.2
An Example
357
9.2.3
Some Issues in Defining Structural Equivalence
359
9.3
Positional Analysis
361
9.3.1
Simplification of
Multirelational
Networks
361
9.3.2
Tasks in a Positional Analysis
363
9.4
Measuring Structural Equivalence
366
9.4.1
Euclidean Distance as a Measure of Structural
Equivalence
367
9.4.2
Correlation as a Measure of Structural Equivalence
368
9.4.3
Some Considerations in Measuring Structural
Equivalence
370
9.5
Representation of Network Positions
375
9.5.1
Partitioning Actors
375
9.5.2
Spatial Representations of Actor Equivalences
385
9.5.3
Ties Between and Within Positions
388
9.6
Summary
391
10 Blockmodels 394
10.1
Definition
395
10.2
Building Blocks
397
10.2.1
Perfect Fit (Fat Fit)
398
10.2.2
Zeroblock (Lean Fit) Criterion
399
10.2.3
Oneblock Criterion
400
10.2.4
α
Density Criterion
400
10.2.5
Comparison of Criteria
401
10.2.6
Examples
401
10.2.7
Valued Relations
406
10.3
Interpretation
408
10.3.1
Actor Attributes
408
10.3.2
Describing Individual Positions
411
10.3.3
Image Matrices
417
10.4
Summary
423
11
Relational Algebras
425
11.1
Background
426
11.2
Notation and Algebraic Operations
428
11.2.1
Composition and Compound Relations
429
11.2.2
Properties of Composition and Compound
Relations
432
11.3
Multiplication Tables for Relations
433
11.3.1
Multiplication Tables and Relational Structures
435
11.3.2
An Example
439
11.4
Simplification of Role Tables
442
11.4.1
Simplification by Comparing Images
443
11.4.2
(gjHomomorphic Reduction
11.5
(^Comparing Role Structures
11.5.1
Joint Homomorphic Reduction
11.5.2
The Common Structure Semigroup
11.5.3
An Example
11.5.4
Measuring the Similarity of Role Structures
11.6
Summary
12
Network Positions and Roles
461
12.1
Background
462
12.1.1
Theoretical Definitions of Roles and Positions
462
12.1.2
Levels of Role Analysis in Social Networks
464
12.1.3
Equivalences in Networks
466
12.2
Structural Equivalence, Revisited
468
12.3
Automorphic and Isomorphic Equivalence
469
12.3.1
Definition
470
12.3.2
Example
471
12.3.3
Measuring Automorphic Equivalence
472
12.4
Regular Equivalence
473
12.4.1
Definition of Regular Equivalence
474
12.4.2
Regular Equivalence for Nondirectional Relations
475
12.4.3
Regular Equivalence
Blockmodels 476
12.4.4
О
A Measure of Regular Equivalence
479
12.4.5
An Example
481
12.5
Types of Ties
483
12.5.1
An Example
485
12.6
Local Role Equivalence
487
12.6.1
Measuring Local Role Dissimilarity
488
12.6.2
Examples
491
12.7
0Ego Algebras
494
12.7.1
Definition of Ego Algebras
496
12.7.2
Equivalence of Ego Algebras
497
12.7.3
Measuring Ego Algebra Similarity
497
12.7.4
Examples
499
12.8
Discussion
502
Part V: Dyadic and Triadic Methods
503
13
Dyads
505
13.1
An Overview
506
13.2
An Example and Some Definitions
508
13.3
Dyads
510
13.3.1
The Dyad Census
512
13.3.2
The Example and Its Dyad Census
513
13.3.3
An Index for Mutuality
514
13.3.4
(g)A Second Index for Mutuality
518
13.3.5
OSubgraph Analysis, in General
520
13.4
Simple Distributions
522
13.4.1
The Uniform Distribution
-
A Review
524
13.4.2
Simple Distributions on Digraphs
526
13.5
Statistical Analysis of the Number of Arcs
528
13.5.1
Testing
529
13.5.2
Estimation
533
13.6
(^Conditional Uniform Distributions
535
13.6.1
Uniform Distribution, Conditional on the Number
of Arcs
536
13.6.2
Uniform Distribution, Conditional on the
Outdegrees
537
13.7
Statistical Analysis of the Number of
Mutuais
539
13.7.1
Estimation
540
13.7.2
Testing
542
13.7.3
Examples
543
13.8
0Other Conditional Uniform Distributions
544
13.8.1
Uniform Distribution, Conditional on the Indegrees
545
13.8.2
The U MAN Distribution
547
13.8.3
More Complex Distributions
550
13.9
Other Research
552
13.10
Conclusion
555
14
Triads
556
14.1
Random Models and Substantive Hypotheses
558
14.2
Triads
559
14.2.1
The Triad Census
564
14.2.2
The Example and Its Triad Census
574
14.3
Distribution of a Triad Census
575
14.3.1
(g)Mean and Variance of a k-subgraph Census
576
14.3.2
Mean and Variance of a Triad Census
14.3.3
Return to the Example
14.3.4
Mean and Variance of Linear Combinations of a
Triad Census
14.3.5
A Brief Review
14.4
Testing Structural Hypotheses
14.4.1
Configurations
14.4.2
From Configurations to Weighting Vectors
14.4.3
From Weighting Vectors to Test Statistics
14.4.4
An Example
14.4.5
Another Example
—
Testing for Transitivity
14.5
Generalizations and Conclusions
14.6
Summary
Part VI: Statistical Dyadic Interaction Models
603
15
Statistical Analysis of Single Relational Networks
605
15.1
Single Directional Relations
607
15.1.1
TheY-array
608
15.1.2
Modeling the Y-array
612
15.1.3
Parameters
619
15.1.4
01s p a Random Directed Graph Distribution?
633
15.1.5
Summary
634
15.2
Attribute Variables
635
15.2.1
Introduction
636
15.2.2
The W-array
637
15.2.3
The Basic Model with Attribute Variables
640
15.2.4
Examples
:
Using Attribute Variables
646
15.3
Related Models for Further Aggregated Data
649
15.3.1
Strict Relational Analysis
—
The V-array
651
15.3.2
Ordinal Relational Data
654
15.4
ONondirectional Relations
656
15.4.1
A Model
656
15.4.2
An Example
657
15.5
(^Recent Generalizations of pi
658
15.6
(^(Single Relations and Two Sets of Actors
662
15.6.1
Introduction
662
15.6.2
The Basic Model
663
15.6.3
Aggregating Dyads for Two-mode Networks
664
15.7 Computing
for Log-linear Models
665
15.7.1
Computing Packages
666
15.7.2
From Printouts to Parameters
671
15.8
Summary
673
16
Stochastic
Blockmodels
and Goodness-of-Fit Indices
675
16.1
Evaluating
Blockmodels 678
16.1.1
Goodness-of-Fit Statistics for
Blockmodels 679
16.1.2
Structurally Based
Blockmodels
and Permutation
Tests
688
16.1.3
An Example
689
16.2
Stochastic
Blockmodels 692
16.2.1
Definition of a Stochastic
Blockmodel 694
16.2.2
Definition of Stochastic Equivalence
696
16.2.3
Application to Special Probability Functions
697
16.2.4
Goodness-of-Fit Indices for Stochastic
Blockmodels 703
16.2.5
OStochastic a posteriori
Blockmodels 706
16.2.6
Measures of Stochastic Equivalence
708
16.2.7
Stochastic Blockmodel Representations
709
16.2.8
The Example Continued
712
16.3
Summary: Generalizations and Extensions
719
16.3.1
Statistical Analysis of Multiple Relational Networks
719
16.3.2
Statistical Analysis of Longitudinal Relations
721
Part
VII:
Epilogue
725
17
Future Directions
727
17.1
Statistical Models
727
17.2
Generalizing to New Kinds of Data
729
17.2.1
Multiple Relations
730
17.2.2
Dynamic and Longitudinal Network Models
730
17.2.3
Ego-centered Networks
731
17.3
Data Collection
731
17.4
Sampling
732
17.5
General Propositions about Structure
732
17.6
Computer Technology
733
17.7
Networks and Standard Social and Behavioral Science
733
Appendix A Computer Programs
735
Appendix
В
Data
738
References
у
756
Name Index
802
Subject Index
у
811
List of Notation
819
Social network
analysis is used widely in the social and behavioral
sciences, as well as in economics, marketing, and industrial engi¬
neering. The social network perspective focuses on relationships
among social entities; examples include communications among
members of a group, economic transactions between corporations,
and trade or treaties among nations. The focus on relationships is an
important addition to standard social and behavioral research,
which is primarily concerned with attributes of the social units.
Social Network Analysis: Methods and Applications reviews and dis¬
cusses methods for the analysis of social networks with a focus on
applications of these methods to many substantive examples. The
book is organized into six parts. The introductory chapters give an
overview of the social network perspective and describe different
kinds of social network data. The second part discusses formal rep¬
resentations for social networks, including notation, graph theory,
and matrix operations. The third part covers structural and loca-
tional properties of social networks, including centrality, prestige,
prominence, structural balance, clusterability, cohesive subgroups,
and affiliation networks. The fourth part examines methods for
social network roles and positions and includes discussions of struc¬
tural equivalence, blockmodels, and relational algebras. The prop¬
erties of dyads and triads are covered in the fifth part of the book,
and the final part discusses statistical methods for social networks.
Social Network Analysis: Methods and Applications is a reference
book that can be used by those who want a comprehensive review of
network methods, or by researchers who have gathered network
data and want to find the most appropriate method by which to
analyze them. It is also intended for use as a textbook, as it is the first
book to provide comprehensive coverage of the methodology and
applications of the field.
|
| adam_txt |
Contents
List of Tables page
xxi
List of Illustrations
xxiv
Preface
xxix
Part I: Networks, Relations, and Structure
1
1
Social Network Analysis in the Social and Behavioral Sciences
3
1.1
The Social Networks Perspective
4
1.2
Historical and Theoretical Foundations
10
1.2.1
Empirical Motivations
11
1.2.2
Theoretical Motivations
13
1.2.3
Mathematical Motivations
15
1.2.4
In Summary
16
1.3
Fundamental Concepts in Network Analysis
17
1.4
Distinctive Features
21
1.5
Organization of the Book and How to Read It
22
1.5.1
Complexity
23
1.5.2
Descriptive and Statistical Methods
23
1.5.3
Theory Driven Methods
24
1.5.4
Chronology
24
1.5.5
Levels of Analysis
25
1.5.6
Chapter Prerequisites
26
1.6
Summary
27
2
Social Network Data
28
2.1
Introduction: What Are Network Data?
28
2.1.1
Structural and Composition Variables
29
2.1.2
Modes
29
2.1.3 Affiliation
Variables
ЗО
2.2
Boundary Specification and Sampling
30
2.2.1
What Is Your Population?
31
2.2.2
Sampling
33
2.3
Types of Networks
35
2.3.1
One-Mode Networks
36
2.3.2
Two-Mode Networks
39
2.3.3
Ego-centered and Special Dyadic Networks
41
2.4
Network Data, Measurement and Collection
43
2.4.1
Measurement
43
2.4.2
Collection
45
2.4.3
Longitudinal Data Collection
55
2.4.4
Measurement Validity, Reliability, Accuracy, Error
56
2.5
Data Sets Found in These Pages
59
2.5.1
Krackhardt's High-tech Managers
60
2.5.2
Padgett's Florentine Families
61
2.5.3
Freeman's
EIES
Network
62
2.5.4
Countries Trade Data
64
2.5.5
Galaskiewicz's CEOs and Clubs Network
65
2.5.6
Other Data
66
Part II: Mathematical Representations of Social Networks
67
3
Notation for Social Network Data
69
3.1
Graph Theoretic Notation
71
3.1.1
A Single Relation
71
3.1.2
OMultiple Relations
73
3.1.3
Summary
75
3.2
Sociometrie
Notation
77
3.2.1
Single Relation
79
3.2.2
Multiple Relations
81
3.2.3
Summary
83
3.3
OAlgebraic Notation
84
3.4
OTwo Sets of Actors
85
3.4.1
(g)Different Types of Pairs
86
3.4.2
OSociometric Notation
87
3.5
Putting It All Together
89
\
Graphs and Matrices
92
4.1
Why Graphs?
93
4.2
Graphs
94
4.2.1
Subgraphs, Dyads, and Triads
97
4.2.2
Nodal Degree
100
4.2.3
Density of Graphs and Subgraphs
101
4.2.4
Example: Padgett's Florentine Families
103
4.2.5
Walks, Trails, and Paths
105
4.2.6
Connected Graphs and Components
109
4.2.7
Geodesies, Distance, and Diameter
110
4.2.8
Connectivity of Graphs
112
4.2.9
Isomorphic Graphs and Subgraphs
117
4.2.10
OSpecial Kinds of Graphs
119
4.3
Directed Graphs
121
4.3.1
Subgraphs
-
Dyads
124
4.3.2
Nodal Indegree and Outdegree
125
4.3.3
Density of a Directed Graph
129
4.3.4
An Example
129
4.3.5
Directed Walks, Paths, Semipaths
129
4.3.6
Reachability and Connectivity in Digraphs
132
4.3.7
Geodesies, Distance and Diameter
134
4.3.8
OSpecial Kinds of Directed Graphs
134
4.3.9
Summary
136
4.4
Signed Graphs and Signed Directed Graphs
136
4.4.1
Signed Graph
137
4.4.2
Signed Directed Graphs
138
4.5
Valued Graphs and Valued Directed Graphs
140
4.5.1
Nodes and Dyads
142
4.5.2
Density in a Valued Graph
143
4.5.3
OPaths in Valued Graphs
143
4.6
Multigraphs
145
4.7
0Hypergraphs
146
4.8
Relations
148
4.8.1
Definition
148
4.8.2
Properties of Relations
149
4.9
Matrices
150
4.9.1
Matrices for Graphs
150
4.9.2
Matrices for Digraphs
152
4.9.3
Matrices for Valued Graphs
153
4.9.4
Matrices for Two-Mode Networks
154
4.9.5
OMatrices for Hypergraphs
4.9.6
Basic Matrix Operations
4.9.7
Computing Simple Network Properties
4.9.8
Summary
4.10
Properties
4.10.1
Reflexivity ;
4.10.2
Symmetry
4.10.3
Transitivity
4.11
Summary
Part III: Structural and Locational Properties
5
Centrality and Prestige
5.1
Prominence: Centrality and Prestige
5.1.1
Actor Centrality
5.1.2
Actor Prestige
5.1.3
Group Centralization and Group Prestige
5.2
Nondirectional Relations
5.2.1
Degree Centrality
5.2.2
Closeness Centrality
5.2.3
Betweenness Centrality
5.2.4
(^Information Centrality
5.3
Directional Relations
5.3.1
Centrality
5.3.2
Prestige
5.3.3
A Different Example
5.4
Comparisons and Extensions
6
Structural Balance and Transitivity
6.1
Structural Balance
6.1.1
Signed Nondirectional Relations
6.1.2
Signed Directional Relations
6.1.3
OChecking for Balance
6.1.4
An Index for Balance
6.1.5
Summary
6.2
Clusterability
6.2.1
The Clustering Theorems
6.2.2
Summary
6.3
Generalizations of Clusterability
6.3.1
Empirical Evidence
239
6.3.2
ORanked Clusterability
240
6.3.3
Summary
242
6.4
Transitivity
243
6.5
Conclusion
247
Cohesive Subgroups
249
7.1
Background
250
7.1.1
Social Group and Subgroup
250
7.1.2
Notation
252
7.2
Subgroups Based on Complete Mutuality
253
7.2.1
Definition of a Clique
254
7.2.2
An Example
254
7.2.3
Considerations
256
7.3
Reachability and Diameter
257
7.3.1
«-cliques
258
7.3.2
An Example
259
7.3.3
Considerations
260
7.3.4
«-clans and
и
-clubs
260
7.3.5
Summary
262
7.4
Subgroups Based on Nodal Degree
263
7.4.1
fc-plexes
265
7.4.2
fe-cores
266
7.5
Comparing Within to Outside Subgroup Ties
267
7.5.1
LS Sets
268
7.5.2
Lambda Sets
269
7.6
Measures of Subgroup Cohesion
270
7.7
Directional Relations
273
7.7.1
Cliques Based on Reciprocated Ties
273
7.7.2
Connectivity in Directional Relations
274
7.7.3
«-cliques in Directional Relations
275
7.8
Valued Relations
277
7.8.1
Cliques, «-cliques, and fe-plexes
278
7.8.2
Other Approaches for Valued Relations
282
7.9
Interpretation of Cohesive Subgroups
283
7.10
Other Approaches
284
7.10.1
Matrix Permutation Approaches
284
7.10.2
Multidimensional Scaling
287
7.10.3
OFactor Analysis
290
7.11
Summary
290
8
Affiliations and Overlapping Subgroups
8.1
Affiliation Networks
8.2
Background
8.2.1
Theory
8.2.2
Concepts
8.2.3
Applications and Rationale
8.3
Representing Affiliation Networks
8.3.1
The Affiliation Network Matrix
8.3.2
Bipartite Graph
8.3.3
Hypergraph
8.3.4
OSimplices and Simplicial Complexes
8.3.5
Summary
8.3.6
An example: Galaskiewicz's CEOs and Clubs
8.4
One-mode Networks
8.4.1
Definition
8.4.2
Examples
8.5
Properties of Affiliation Networks
8.5.1
Properties of Actors and Events
8.5.2
Properties of One-mode Networks
8.5.3
Taking Account of Subgroup Size
8.5.4
Interpretation
8.6
(^Analysis of Actors and Events
8.6.1
(g)Galois Lattices
8.6.2
(^Correspondence Analysis
8.7
Summary
Part IV: Roles and Positions
345
9
Structural Equivalence
347
9.1
Background
348
9.1.1
Social Roles and Positions
348
9.1.2
An Overview of Positional and Role Analysis
351
9.1.3
A Brief History
354
9.2
Definition of Structural Equivalence
356
9.2.1
Definition
356
9.2.2
An Example
357
9.2.3
Some Issues in Defining Structural Equivalence
359
9.3
Positional Analysis
361
9.3.1
Simplification of
Multirelational
Networks
361
9.3.2
Tasks in a Positional Analysis
363
9.4
Measuring Structural Equivalence
366
9.4.1
Euclidean Distance as a Measure of Structural
Equivalence
367
9.4.2
Correlation as a Measure of Structural Equivalence
368
9.4.3
Some Considerations in Measuring Structural
Equivalence
370
9.5
Representation of Network Positions
375
9.5.1
Partitioning Actors
375
9.5.2
Spatial Representations of Actor Equivalences
385
9.5.3
Ties Between and Within Positions
388
9.6
Summary
391
10 Blockmodels 394
10.1
Definition
395
10.2
Building Blocks
397
10.2.1
Perfect Fit (Fat Fit)
398
10.2.2
Zeroblock (Lean Fit) Criterion
399
10.2.3
Oneblock Criterion
400
10.2.4
α
Density Criterion
400
10.2.5
Comparison of Criteria
401
10.2.6
Examples
401
10.2.7
Valued Relations
406
10.3
Interpretation
408
10.3.1
Actor Attributes
408
10.3.2
Describing Individual Positions
411
10.3.3
Image Matrices
417
10.4
Summary
423
11
Relational Algebras
425
11.1
Background
426
11.2
Notation and Algebraic Operations
428
11.2.1
Composition and Compound Relations
429
11.2.2
Properties of Composition and Compound
Relations
432
11.3
Multiplication Tables for Relations
433
11.3.1
Multiplication Tables and Relational Structures
435
11.3.2
An Example
439
11.4
Simplification of Role Tables
442
11.4.1
Simplification by Comparing Images
443
11.4.2
(gjHomomorphic Reduction
11.5
(^Comparing Role Structures
11.5.1
Joint Homomorphic Reduction
11.5.2
The Common Structure Semigroup
11.5.3
An Example
11.5.4
Measuring the Similarity of Role Structures
11.6
Summary
12
Network Positions and Roles
461
12.1
Background
462
12.1.1
Theoretical Definitions of Roles and Positions
462
12.1.2
Levels of Role Analysis in Social Networks
464
12.1.3
Equivalences in Networks
466
12.2
Structural Equivalence, Revisited
468
12.3
Automorphic and Isomorphic Equivalence
469
12.3.1
Definition
470
12.3.2
Example
471
12.3.3
Measuring Automorphic Equivalence
472
12.4
Regular Equivalence
473
12.4.1
Definition of Regular Equivalence
474
12.4.2
Regular Equivalence for Nondirectional Relations
475
12.4.3
Regular Equivalence
Blockmodels 476
12.4.4
О
A Measure of Regular Equivalence
479
12.4.5
An Example
481
12.5
"Types" of Ties
483
12.5.1
An Example
485
12.6
Local Role Equivalence
487
12.6.1
Measuring Local Role Dissimilarity
488
12.6.2
Examples
491
12.7
0Ego Algebras
494
12.7.1
Definition of Ego Algebras
496
12.7.2
Equivalence of Ego Algebras
497
12.7.3
Measuring Ego Algebra Similarity
497
12.7.4
Examples
499
12.8
Discussion
502
Part V: Dyadic and Triadic Methods
503
13
Dyads
505
13.1
An Overview
506
13.2
An Example and Some Definitions
508
13.3
Dyads
510
13.3.1
The Dyad Census
512
13.3.2
The Example and Its Dyad Census
513
13.3.3
An Index for Mutuality
514
13.3.4
(g)A Second Index for Mutuality
518
13.3.5
OSubgraph Analysis, in General
520
13.4
Simple Distributions
522
13.4.1
The Uniform Distribution
-
A Review
524
13.4.2
Simple Distributions on Digraphs
526
13.5
Statistical Analysis of the Number of Arcs
528
13.5.1
Testing
529
13.5.2
Estimation
533
13.6
(^Conditional Uniform Distributions
535
13.6.1
Uniform Distribution, Conditional on the Number
of Arcs
536
13.6.2
Uniform Distribution, Conditional on the
Outdegrees
537
13.7
Statistical Analysis of the Number of
Mutuais
539
13.7.1
Estimation
540
13.7.2
Testing
542
13.7.3
Examples
543
13.8
0Other Conditional Uniform Distributions
544
13.8.1
Uniform Distribution, Conditional on the Indegrees
545
13.8.2
The U\MAN Distribution
547
13.8.3
More Complex Distributions
550
13.9
Other Research
552
13.10
Conclusion
555
14
Triads
556
14.1
Random Models and Substantive Hypotheses
558
14.2
Triads
559
14.2.1
The Triad Census
564
14.2.2
The Example and Its Triad Census
574
14.3
Distribution of a Triad Census
575
14.3.1
(g)Mean and Variance of a k-subgraph Census
576
14.3.2
Mean and Variance of a Triad Census
14.3.3
Return to the Example
14.3.4
Mean and Variance of Linear Combinations of a
Triad Census
14.3.5
A Brief Review
14.4
Testing Structural Hypotheses
14.4.1
Configurations
14.4.2
From Configurations to Weighting Vectors
14.4.3
From Weighting Vectors to Test Statistics
14.4.4
An Example
14.4.5
Another Example
—
Testing for Transitivity
14.5
Generalizations and Conclusions
14.6
Summary
Part VI: Statistical Dyadic Interaction Models
603
15
Statistical Analysis of Single Relational Networks
605
15.1
Single Directional Relations
607
15.1.1
TheY-array
608
15.1.2
Modeling the Y-array
612
15.1.3
Parameters
619
15.1.4
01s p\ a Random Directed Graph Distribution?
633
15.1.5
Summary
634
15.2
Attribute Variables
635
15.2.1
Introduction
636
15.2.2
The W-array
637
15.2.3
The Basic Model with Attribute Variables
640
15.2.4
Examples
:
Using Attribute Variables
646
15.3
Related Models for Further Aggregated Data
649
15.3.1
Strict Relational Analysis
—
The V-array
651
15.3.2
Ordinal Relational Data
654
15.4
ONondirectional Relations
656
15.4.1
A Model
656
15.4.2
An Example
657
15.5
(^Recent Generalizations of pi
658
15.6
(^(Single Relations and Two Sets of Actors
662
15.6.1
Introduction
662
15.6.2
The Basic Model
663
15.6.3
Aggregating Dyads for Two-mode Networks
664
15.7 Computing
for Log-linear Models
665
15.7.1
Computing Packages
666
15.7.2
From Printouts to Parameters
671
15.8
Summary
673
16
Stochastic
Blockmodels
and Goodness-of-Fit Indices
675
16.1
Evaluating
Blockmodels 678
16.1.1
Goodness-of-Fit Statistics for
Blockmodels 679
16.1.2
Structurally Based
Blockmodels
and Permutation
Tests
688
16.1.3
An Example
689
16.2
Stochastic
Blockmodels 692
16.2.1
Definition of a Stochastic
Blockmodel 694
16.2.2
Definition of Stochastic Equivalence
696
16.2.3
Application to Special Probability Functions
697
16.2.4
Goodness-of-Fit Indices for Stochastic
Blockmodels 703
16.2.5
OStochastic a posteriori
Blockmodels 706
16.2.6
Measures of Stochastic Equivalence
708
16.2.7
Stochastic Blockmodel Representations
709
16.2.8
The Example Continued
712
16.3
Summary: Generalizations and Extensions
719
16.3.1
Statistical Analysis of Multiple Relational Networks
719
16.3.2
Statistical Analysis of Longitudinal Relations
721
Part
VII:
Epilogue
725
17
Future Directions
727
17.1
Statistical Models
727
17.2
Generalizing to New Kinds of Data
729
17.2.1
Multiple Relations
730
17.2.2
Dynamic and Longitudinal Network Models
730
17.2.3
Ego-centered Networks
731
17.3
Data Collection
731
17.4
Sampling
732
17.5
General Propositions about Structure
732
17.6
Computer Technology
733
17.7
Networks and Standard Social and Behavioral Science
733
Appendix A Computer Programs
735
Appendix
В
Data
738
References
у
756
Name Index
802
Subject Index
у
811
List of Notation
819
Social network
analysis is used widely in the social and behavioral
sciences, as well as in economics, marketing, and industrial engi¬
neering. The social network perspective focuses on relationships
among social entities; examples include communications among
members of a group, economic transactions between corporations,
and trade or treaties among nations. The focus on relationships is an
important addition to standard social and behavioral research,
which is primarily concerned with attributes of the social units.
Social Network Analysis: Methods and Applications reviews and dis¬
cusses methods for the analysis of social networks with a focus on
applications of these methods to many substantive examples. The
book is organized into six parts. The introductory chapters give an
overview of the social network perspective and describe different
kinds of social network data. The second part discusses formal rep¬
resentations for social networks, including notation, graph theory,
and matrix operations. The third part covers structural and loca-
tional properties of social networks, including centrality, prestige,
prominence, structural balance, clusterability, cohesive subgroups,
and affiliation networks. The fourth part examines methods for
social network roles and positions and includes discussions of struc¬
tural equivalence, blockmodels, and relational algebras. The prop¬
erties of dyads and triads are covered in the fifth part of the book,
and the final part discusses statistical methods for social networks.
Social Network Analysis: Methods and Applications is a reference
book that can be used by those who want a comprehensive review of
network methods, or by researchers who have gathered network
data and want to find the most appropriate method by which to
analyze them. It is also intended for use as a textbook, as it is the first
book to provide comprehensive coverage of the methodology and
applications of the field. |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Wasserman, Stanley Faust, Katherine |
| author_GND | (DE-588)13036018X |
| author_facet | Wasserman, Stanley Faust, Katherine |
| author_role | aut aut |
| author_sort | Wasserman, Stanley |
| author_variant | s w sw k f kf |
| building | Verbundindex |
| bvnumber | BV022936234 |
| classification_rvk | MR 2000 MR 2400 ST 650 |
| classification_tum | SOZ 720f |
| ctrlnum | (OCoLC)405123856 (DE-599)BVBBV022936234 |
| discipline | Informatik Soziologie |
| discipline_str_mv | Informatik Soziologie |
| edition | 1. publ., repr. |
| format | Book |
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| id | DE-604.BV022936234 |
| illustrated | Illustrated |
| index_date | 2024-07-02T18:56:39Z |
| indexdate | 2024-07-09T21:08:02Z |
| institution | BVB |
| isbn | 9780521382694 9780521387071 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016140974 |
| oclc_num | 405123856 |
| open_access_boolean | |
| owner | DE-739 DE-473 DE-BY-UBG DE-19 DE-BY-UBM |
| owner_facet | DE-739 DE-473 DE-BY-UBG DE-19 DE-BY-UBM |
| physical | XXXI, 825 S. graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| series | Structural analysis in the social sciences |
| series2 | Structural analysis in the social sciences |
| spelling | Wasserman, Stanley Verfasser (DE-588)13036018X aut Social network analysis methods and applications Stanley Wasserman ; Katherine Faust 1. publ., repr. Cambridge [u.a.] Cambridge Univ. Press 2007 XXXI, 825 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Structural analysis in the social sciences 8 Hier auch später erschienene, unveränderte Nachdrucke Analiz - 880-05 - metodi Izsledvanii͡a - 880-07 - metodologii͡a Sot͡sialni nauki - 880-06 - izsledvanii͡a - metodologii͡a Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd rswk-swf Netzwerkanalyse Soziologie (DE-588)4205975-6 s DE-604 Faust, Katherine Verfasser aut Structural analysis in the social sciences 8 (DE-604)BV002814947 8 Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016140974&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016140974&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Wasserman, Stanley Faust, Katherine Social network analysis methods and applications Structural analysis in the social sciences Analiz - 880-05 - metodi Izsledvanii͡a - 880-07 - metodologii͡a Sot͡sialni nauki - 880-06 - izsledvanii͡a - metodologii͡a Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd |
| subject_GND | (DE-588)4205975-6 |
| title | Social network analysis methods and applications |
| title_auth | Social network analysis methods and applications |
| title_exact_search | Social network analysis methods and applications |
| title_exact_search_txtP | Social network analysis methods and applications |
| title_full | Social network analysis methods and applications Stanley Wasserman ; Katherine Faust |
| title_fullStr | Social network analysis methods and applications Stanley Wasserman ; Katherine Faust |
| title_full_unstemmed | Social network analysis methods and applications Stanley Wasserman ; Katherine Faust |
| title_short | Social network analysis |
| title_sort | social network analysis methods and applications |
| title_sub | methods and applications |
| topic | Analiz - 880-05 - metodi Izsledvanii͡a - 880-07 - metodologii͡a Sot͡sialni nauki - 880-06 - izsledvanii͡a - metodologii͡a Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd |
| topic_facet | Analiz - 880-05 - metodi Izsledvanii͡a - 880-07 - metodologii͡a Sot͡sialni nauki - 880-06 - izsledvanii͡a - metodologii͡a Netzwerkanalyse Soziologie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016140974&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016140974&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV002814947 |
| work_keys_str_mv | AT wassermanstanley socialnetworkanalysismethodsandapplications AT faustkatherine socialnetworkanalysismethodsandapplications |