Understanding Large Temporal Networks and Spatial Networks

Informationen zum Autor Vladimir Batagelj, Department of Mathematics, Faculty of Mathematics and Physics, University of Ljubljana, Slovenia Patrick Doreian, Faculty of Social Sciences, University of Ljubljana, Slovenia andDepartment of Sociology, University of Pittsburgh, USA Anuska Ferligoj, Faculty of Social Sciences, University of Ljubljana, Slovenia Natasa Kejzar, Faculty of Medicine, Institute for Biostatistics and Medical Informatics, University of Ljubljana, Slovenia Klappentext This book explores social mechanisms that drive network change and link them to computationally sound models of changing structure to detect patterns. This text identifies the social processes generating these networks and how networks have evolved. Zusammenfassung A comprehensive, sweeping work by an acclaimed team of authors at the forefront of this hot topic, Understanding Large Temporal Networks and Spatial Networks explores the different approaches to studying large temporal and spatial networks and links them to computationally sound models of changing structure to detect patterns. Inhaltsverzeichnis Preface xiii1 Temporal and Spatial Networks 11.1 Modern Social Network Analysis 11.2 Network Sizes 31.3 Substantive Concerns 31.3.1 Citation Networks 31.3.2 Other Types of Large Networks 71.4 Computational Methods 101.5 Data for Large Temporal Networks 121.5.1 The Main Datasets 121.5.2 Secondary Datasets 141.6 Induction and Deduction 162 Foundations of Methods for Large Networks 182.1 Networks 182.1.1 Descriptions of Networks 202.1.2 Degrees 212.1.3 Descriptions of Properties 212.1.4 Visualizations of Properties 222.2 Types of Networks 222.2.1 Temporal Networks 232.2.2 Multirelational Networks 252.2.3 Two-mode Networks 282.3 Large Networks 282.3.1 Small and Middle Sized Networks 292.3.2 Large Networks 302.3.3 Complexity of Algorithms 302.4 Strategies for Analyzing Large Networks 322.5 Statistical Network Measures 332.5.1 Using Pajek and R Together 352.5.2 Fitting Distributions 352.6 Subnetworks 372.6.1 Clusters, Clusterings, Partitions, Hierarchies 372.6.2 Contractions of Clusters 382.6.3 Subgraphs 402.6.4 Cuts 422.7 Connectivity Properties of Networks 462.7.1 Walks 462.7.2 Equivalence Relations and Partitions 472.7.3 Connectivity 482.7.4 Condensation 492.7.5 Bow-tie Structure of the Web Graph 502.7.6 The Internal Structure of Strong Components 512.7.7 Bi-connectivity and -connectivity 512.8 Triangular and Short Cycle Connectivities 532.9 Islands 542.9.1 Defining Islands 552.9.2 Some Properties of Islands 562.10 Cores and Generalized Cores 572.10.1 Cores 582.10.2 Generalized Cores 592.11 Important Vertices in Networks 612.11.1 Degrees, Closeness, Betweenness and Other Indices 632.11.2 Clustering 652.11.3 Computing Further Indices Through Functions 662.12 Transition to Methods for Large Networks 683 Methods for Large Networks 693.1 Acyclic Networks 713.1.1 Some Basic Properties of Acyclic Networks 713.1.2 Compatible Numberings: Depth and Topological Order 723.1.3 Topological Orderings and Functions on Acyclic Networks 743.2 SPC Weights in Acyclic Networks 753.2.1 Citation Networks 753.2.2 Analysis of Citation Networks 763.2.3 Search Path Count Method 773.2.4 Computing SPLC and SPNP Weights 773.2.5 Implementation Details 783.2.6 Vertex Weights 783.2.7 General Properties of Weights 793.2.8 SPC Weights 803.3 Probabilistic Flow in Acyclic Network 813.4 Nonacyclic Citation Networks 823.5 Two-mode Networks from Data Tables 843.5.1 Multiplication of Two-mode Networks 853.6 Bibliographic Networks 883.6.1 Co-authorship Networks 883.6.2 Collaboration Networks 893.6.3 Other Derived Networks 923.7 Weights 943.7.1 Normalizations of Weights 943.7.2 -Rings 943.7.3 4-Rings and Analysis of Two-mode Networks 953.7.4 T...

Inhaltsverzeichnis

Preface xiii
 
1 Temporal and Spatial Networks 1
 
1.1 Modern Social Network Analysis 1
 
1.2 Network Sizes 3
 
1.3 Substantive Concerns 3
 
1.3.1 Citation Networks 3
 
1.3.2 Other Types of Large Networks 7
 
1.4 Computational Methods 10
 
1.5 Data for Large Temporal Networks 12
 
1.5.1 The Main Datasets 12
 
1.5.2 Secondary Datasets 14
 
1.6 Induction and Deduction 16
 
2 Foundations of Methods for Large Networks 18
 
2.1 Networks 18
 
2.1.1 Descriptions of Networks 20
 
2.1.2 Degrees 21
 
2.1.3 Descriptions of Properties 21
 
2.1.4 Visualizations of Properties 22
 
2.2 Types of Networks 22
 
2.2.1 Temporal Networks 23
 
2.2.2 Multirelational Networks 25
 
2.2.3 Two-mode Networks 28
 
2.3 Large Networks 28
 
2.3.1 Small and Middle Sized Networks 29
 
2.3.2 Large Networks 30
 
2.3.3 Complexity of Algorithms 30
 
2.4 Strategies for Analyzing Large Networks 32
 
2.5 Statistical Network Measures 33
 
2.5.1 Using Pajek and R Together 35
 
2.5.2 Fitting Distributions 35
 
2.6 Subnetworks 37
 
2.6.1 Clusters, Clusterings, Partitions, Hierarchies 37
 
2.6.2 Contractions of Clusters 38
 
2.6.3 Subgraphs 40
 
2.6.4 Cuts 42
 
2.7 Connectivity Properties of Networks 46
 
2.7.1 Walks 46
 
2.7.2 Equivalence Relations and Partitions 47
 
2.7.3 Connectivity 48
 
2.7.4 Condensation 49
 
2.7.5 Bow-tie Structure of the Web Graph 50
 
2.7.6 The Internal Structure of Strong Components 51
 
2.7.7 Bi-connectivity and -connectivity 51
 
2.8 Triangular and Short Cycle Connectivities 53
 
2.9 Islands 54
 
2.9.1 Defining Islands 55
 
2.9.2 Some Properties of Islands 56
 
2.10 Cores and Generalized Cores 57
 
2.10.1 Cores 58
 
2.10.2 Generalized Cores 59
 
2.11 Important Vertices in Networks 61
 
2.11.1 Degrees, Closeness, Betweenness and Other Indices 63
 
2.11.2 Clustering 65
 
2.11.3 Computing Further Indices Through Functions 66
 
2.12 Transition to Methods for Large Networks 68
 
3 Methods for Large Networks 69
 
3.1 Acyclic Networks 71
 
3.1.1 Some Basic Properties of Acyclic Networks 71
 
3.1.2 Compatible Numberings: Depth and Topological Order 72
 
3.1.3 Topological Orderings and Functions on Acyclic Networks 74
 
3.2 SPC Weights in Acyclic Networks 75
 
3.2.1 Citation Networks 75
 
3.2.2 Analysis of Citation Networks 76
 
3.2.3 Search Path Count Method 77
 
3.2.4 Computing SPLC and SPNP Weights 77
 
3.2.5 Implementation Details 78
 
3.2.6 Vertex Weights 78
 
3.2.7 General Properties of Weights 79
 
3.2.8 SPC Weights 80
 
3.3 Probabilistic Flow in Acyclic Network 81
 
3.4 Nonacyclic Citation Networks 82
 
3.5 Two-mode Networks from Data Tables 84
 
3.5.1 Multiplication of Two-mode Networks 85
 
3.6 Bibliographic Networks 88
 
3.6.1 Co-authorship Networks 88
 
3.6.2 Collaboration Networks 89
 
3.6.3 Other Derived Networks 92
 
3.7 Weights 94
 
3.7.1 Normalizations of Weights 94
 
3.7.2 -Rings 94
 
3.7.3 4-Rings and Analysis of Two-mode Networks 95
 
3.7.4 Two-mode Cores 96
 
3.8 Pathfinder 96
 
3.8.1 Pathfinder Algorithms 100
 
3.8.2 Computing the Closure Over the Pathfinder Semiring 101
 
3.8.3 Spanish Algorithms 101
 

Über den Autor / die Autorin

Vladimir Batagelj
Department of Mathematics, Faculty of Mathematics and Physics, University of Ljubljana, Slovenia

Patrick Doreian
Faculty of Social Sciences, University of Ljubljana, Slovenia and Department of Sociology, University of Pittsburgh, USA

Anuska Ferligoj
Faculty of Social Sciences, University of Ljubljana, Slovenia

Natasa Kej?ar
Faculty of Medicine, Institute for Biostatistics and Medical Informatics, University of Ljubljana, Slovenia

Zusammenfassung

This book explores social mechanisms that drive network change and link them to computationally sound models of changing structure to detect patterns. This text identifies the social processes generating these networks and how networks have evolved.

Produktdetails

• Autoren: Vladimi Batagelj, Vladimir Batagelj, Patric Doreian, Patrick Doreian, Patrick Batagelj Doreian, PP Doreian, DOREIAN PATRICK BATAGELJ VLADIMI, An Ferligoj, Anus Ferligoj, Anuska Ferligoj, Natasa Kejzar
• Verlag: Blackwell Scientific Publishers Ltd
• Sprache: Englisch
• Produktform: Fester Einband
• Erschienen: 31.10.2014
• EAN: 9780470714522
• ISBN: 978-0-470-71452-2
• Abmessung: 177 mm x 250 mm x 26 mm
• Serien: Wiley Series in Computational and Quantitative Social Science; Wiley Series in Computational and Quantitative Social Science
• Themen: Naturwissenschaften, Medizin, Informatik, Technik > Mathematik > Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Schule und Lernen > Unterrichtsvorbereitung > Berufliche Bildung; Statistik, Soziologie, Biometrie, Sociology, Statistics, Biometrics, Allg. Soziologie, Angewandte Wahrscheinlichkeitsrechnung u. Statistik, Applied Probability & Statistics