Verfügbarkeit: Social network analysis

Social network analysis: methods and applications

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Bibliographische Detailangaben
Hauptverfasser:	Wasserman, Stanley (VerfasserIn), Faust, Katherine (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Cambridge [u.a.] Cambridge Univ. Press 2008
Ausgabe:	1. publ., 17. print.
Schriftenreihe:	Structural analysis in the social sciences 8
Schlagworte:	Netzwerkanalyse > Soziologie
Online-Zugang:	Inhaltsverzeichnis
Beschreibung:	XXXI, 825 S. graph. Darst.
ISBN:	9780521382694 9780521387071

Internformat

MARC


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Datensatz im Suchindex

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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 ix χ Contents 2.1.2 Modes 29 2.1.3 Affiliation Variables 30 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 Π: 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 Contents xi 4 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 xii Contents 4.9.5 OMatrices for Hypergraphs 154 4.9.6 Basic Matrix Operations 154 4.9.7 Computing Simple Network Properties 159 4.9.8 Summary 164 4.10 Properties 164 4.10.1 Reflexivity 164 4.10.2 Symmetry 165 4.10.3 Transitivity 165 4.11 Summary 165 Part III: Structural and Locational Properties 167 5 Centrality and Prestige 169 5.1 Prominence: Centrality and Prestige 172 5.1.1 Actor Centrality 173 5.1.2 Actor Prestige 174 5.1.3 Group Centralization and Group Prestige 175 5.2 Nondirectional Relations 177 5.2.1 Degree Centrality 178 5.2.2 Closeness Centrality 183 5.2.3 Betweenness Centrality 188 5.2.4 (^Information Centrality 192 5.3 Directional Relations 198 5.3.1 Centrality 199 5.3.2 Prestige 202 5.3.3 A Different Example 210 5.4 Comparisons and Extensions 215 б Structural Balance and Transitivity 220 6.1 Structural Balance 222 6.1.1 Signed Nondirectional Relations 223 6.1.2 Signed Directional Relations 228 6.1.3 OChecking for Balance 230 6.1.4 An Index for Balance 232 6.1.5 Summary 232 6.2 Clusterability 233 6.2.1 The Clustering Theorems 235 6.2.2 Summary 238 6.3 Generalizations of Clusterability 239 Contents xiii 6J.1 bmpirical bvidence 239 6.3.2 ORanked Clusterability 240 6.3.3 Summary 242 6.4 Transitivity 243 6.5 Conclusion 247 7 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 n-cliques 258 7.3.2 An Example 259 7.3.3 Considerations 260 7.3.4 η -clans and n-clubs 260 7.3.5 Summary 262 7.4 Subgroups Based on Nodal Degree 263 7.4.1 fc-plexes 265 7.4.2 fc-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 η -cliques in Directional Relations 275 7.8 Valued Relations 277 7.8.1 Cliques, η -cliques, and fc-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 xiv Contents 8 Affiliations and Overlapping Subgroups 291 8.1 Affiliation Networks 291 8.2 Background 292 8.2.1 Theory 292 8.2.2 Concepts 294 8.2.3 Applications and Rationale 295 8.3 Representing Affiliation Networks 298 8.3.1 The Affiliation Network Matrix 298 8.3.2 Bipartite Graph 299 8.3.3 Hypergraph 303 8.3.4 OSimplices and Simplicial Complexes 306 8.3.5 Summary 306 8.3.6 An example: Galaskiewicz s CEOs and Clubs 307 8.4 One-mode Networks 307 8.4.1 Definition 307 8.4.2 Examples 309 8.5 Properties of Affiliation Networks 312 8.5.1 Properties of Actors and Events 312 8.5.2 Properties of One-mode Networks 314 8.5.3 Taking Account of Subgroup Size 322 8.5.4 Interpretation 324 8.6 <g)Analysis of Actors and Events 326 8.6.1 <g)Galois Lattices 326 8.6.2 (^Correspondence Analysis 334 8.7 Summary 342 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 Contents xv 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 xvi Contents 11.4.2 (gjHomomorphic Reduction 445 11.5 (gjComparing Role Structures 449 11.5.1 Joint Homomorphic Reduction 451 11.5.2 The Common Structure Semigroup 452 11.5.3 An Example 453 11.5.4 Measuring the Similarity of Role Structures 457 11.6 Summary 460 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 ΟΑ 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 ®Ego 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 Contents xvii 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 <g)Other 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 ^-subgraph Census 576 xviii Contents 14.3.2 Mean and Variance of a Triad Census 579 14.3.3 Return to the Example 581 14.3.4 Mean and Variance of Linear Combinations of a Triad Census 582 14.3.5 Â Brief Review 584 14.4 Testing Structural Hypotheses 585 14.4.1 Configurations 585 14.4.2 From Configurations to Weighting Vectors 590 14.4.3 From Weighting Vectors to Test Statistics 592 14.4.4 An Example 595 14.4.5 Another Example — Testing for Transitivity 596 14.5 Generalizations and Conclusions 598 14.6 Summary 601 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 (g)Is pi 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 pt 658 15.6 <g)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 Contents xix 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 xx Contents Appendix A Computer Programs 735 Appendix В Data 738 References 756 Name Index 802 Subject Index 811 List of Notation 819
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spelling	Wasserman, Stanley Verfasser (DE-588)13036018X aut Social network analysis methods and applications Stanley Wasserman ; Katherine Faust 1. publ., 17. print. Cambridge [u.a.] Cambridge Univ. Press 2008 XXXI, 825 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Structural analysis in the social sciences 8 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 Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017595183&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Wasserman, Stanley Faust, Katherine Social network analysis methods and applications Structural analysis in the social sciences 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_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	Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd
topic_facet	Netzwerkanalyse Soziologie
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017595183&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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