Advances in Network Clustering and Blockmodeling

1. Auflage 2020
ISBN: 978-1-119-22470-9
Verlag: Wiley

Buch, Englisch, 432 Seiten, Format (B × H): 175 mm x 246 mm, Gewicht: 907 g

Provides an overview of the developments and advances in the field of network clustering and blockmodeling over the last 10 years

This book offers an integrated treatment of network clustering and blockmodeling, covering all of the newest approaches and methods that have been developed over the last decade. Presented in a comprehensive manner, it offers the foundations for understanding network structures and processes, and features a wide variety of new techniques addressing issues that occur during the partitioning of networks across multiple disciplines such as community detection, blockmodeling of valued networks, role assignment, and stochastic blockmodeling.

Written by a team of international experts in the field, Advances in Network Clustering and Blockmodeling offers a plethora of diverse perspectives covering topics such as: bibliometric analyses of the network clustering literature; clustering approaches to networks; label propagation for clustering; and treating missing network data before partitioning. It also examines the partitioning of signed networks, multimode networks, and linked networks. A chapter on structured networks and coarsegrained descriptions is presented, along with another on scientific coauthorship networks. The book finishes with a section covering conclusions and directions for future work. In addition, the editors provide numerous tables, figures, case studies, examples, datasets, and more.
- Offers a clear and insightful look at the state of the art in network clustering and blockmodeling
- Provides an excellent mix of mathematical rigor and practical application in a comprehensive manner
- Presents a suite of new methods, procedures, algorithms for partitioning networks, as well as new techniques for visualizing matrix arrays
- Features numerous examples throughout, enabling readers to gain a better understanding of research methods and to conduct their own research effectively
- Written by leading contributors in the field of spatial networks analysis

Advances in Network Clustering and Blockmodeling is an ideal book for graduate and undergraduate students taking courses on network analysis or working with networks using real data. It will also benefit researchers and practitioners interested in network analysis.

Autoren/Hrsg.

Doreian, Patrick

Batagelj, Vladimir

Ferligoj, Anuska

Fachgebiete

• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
• Mathematik | Informatik Mathematik Stochastik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

List of Contributors xv

1 Introduction 1
Patrick Doreian, Vladimir Batagelj, and Anuška Ferligoj

1.1 On the Chapters 1

1.2 Looking Forward 9

2 Bibliometric Analyses of the Network Clustering Literature 11
Vladimir Batagelj, Anuška Ferligoj, and Patrick Doreian

2.1 Introduction 11

2.2 Data Collection and Cleaning 12

2.2.1 Most Cited/Citing Works 15

2.2.2 The Boundary Problem for Citation Networks 17

2.3 Analyses of the Citation Networks 19

2.3.1 Components 20

2.3.2 The CPM Path of the Main Citation Network 20

2.3.3 Key-Route Paths 20

2.3.4 Positioning Sets of Selected Works in a Citation Network 30

2.4 Link Islands in the Clustering Network Literature 35

2.4.1 Island 10: Community Detection and Blockmodeling 35

2.4.2 Island 7: Engineering Geology 36

2.4.3 Island 9: Geophysics 38

2.4.4 Island 2: Electromagnetic Fields and their Impact on Humans 38

2.4.5 Limitations and Extensions 40

2.5 Authors 41

2.5.1 Productivity Inside Research Groups 42

2.5.2 Collaboration 43

2.5.3 Citations Among Authors Contributing to the Network Partitioning Literature 45

2.5.4 Citations Among Journals 47

2.5.5 Bibliographic Coupling 50

2.5.6 Linking Through a Jaccard Network 58

2.6 Summary and Future Work 62

Acknowledgements 63

References 63

3 Clustering Approaches to Networks 65
Vladimir Batagelj

3.1 Introduction 65

3.2 Clustering 66

3.2.1 The Clustering Problem 66

3.2.2 Criterion Functions 67

3.2.3 Cluster-Error Function/Examples 72

3.2.4 The Complexity of the Clustering Problem 75

3.3 Approaches to Clustering 76

3.3.1 Local Optimization 76

3.3.2 Dynamic Programming 79

3.3.3 Hierarchical Methods 79

3.3.4 Adding Hierarchical Methods 83

3.3.5 The Leaders Method 84

3.4 Clustering Graphs and Networks 87

3.5 Clustering in Graphs and Networks 89

3.5.1 An Indirect Approach 89

3.5.2 A Direct Approach: Blockmodeling 90

3.5.3 Graph Theoretic Approaches 90

3.6 Agglomerative Method for Relational Constraints 90

3.6.1 Software Support 95

3.7 Some Examples 95

3.7.1 The US Geographical Data, 2016 95

3.7.2 Citations Among Authors from the Network Clustering Literature 98

3.8 Conclusion 102

Acknowledgements 102

References 102

4 Different Approaches to Community Detection 105
Martin Rosvall, Jean-Charles Delvenne, Michael T. Schaub, and Renaud Lambiotte

4.1 Introduction 105

4.2 Minimizing Constraint Violations: the Cut-based Perspective 107

4.3 Maximizing Internal Density: the Clustering Perspective 108

4.4 Identifying Structural Equivalence: the Stochastic Block Model Perspective 110

4.5 Identifying Coarse-grained Descriptions: the Dynamical Perspective 111

4.6 Discussion 114

4.7 Conclusions 116

Acknowledgements 116

References 116

5 Label Propagation for Clustering 121
Lovro Šubelj

5.1 Label Propagation Method 121

5.1.1 Resolution of Label Ties 123

5.1.2 Order of Label Propagation 123

5.1.3 Label Equilibrium Criterium 124

5.1.4 Algorithm and Complexity 125

5.2 Label Propagation as Optimization 127

5.3 Advances of Label Propagation 128

5.3.1 Label Propagation Under Constraints 129

5.3.2 Label Propagation with Preferences 130

5.3.3 Method Stability and Complexity 133

5.4 Extensions to Other Networks 137

5.5 Alternative Types of Network Structures 139

5.5.1 Overlapping Groups of Nodes 139

5.5.2 Hierarchy of Groups of Nodes 140

5.5.3 Structural Equivalence Groups 142

5.6 Applications of Label Propagation 146

5.7 Summary and Outlook 146

References 147

6 Blockmodeling of Valued Networks 151
Carl

Über Autor(innen)

Patrick Doreian, MA, is Professor Emeritus of Sociology and Statistics at the University of Pittsburgh and has a research position at the Faculty of Social Sciences at the University of Ljubljana. He has published over 150 articles in academic journals as well as nine books and numerous book chapters. His co-authored book Generalized Blockmodeling written with Vladimir Batagelj and Anuška Ferligoj received the Harrison White Outstanding Book Award in 2007. He is an honorary Senator of the University of Ljubljana, Slovenia.
Vladimir Batagelj, PhD, is Professor Emeritus of Discrete and Computational Mathematics from the University of Ljubljana, Slovenia. He is Senior Researcher at the Department of Theoretical Computer Science of IMFM, Ljubljana, the Institute Andrej Marušic at University of Primorska, Koper, and NRU HSE International Laboratory for Applied Network Research, Moscow. He is a co-author of program Pajek for large network analysis and visualization. He is an elected member of the International Statistical Institute. With Patrick Doreian, Anuška Ferligoj and Nataša Kej??ar he co-authored the book Understanding Large Temporal Networks and Spatial Networks, Wiley, 2014.
Anuška Ferligoj, PhD, is Professor of Statistics at the Faculty of Social Sciences at the University of Ljubljana and academic supervisor at the NRU HSE International Laboratory for Applied Network Research, Moscow. She is a member of the European Academy of Sociology. In 2010 she received the Doctor et Professor Honoris Causa at the Eötvös Loránd University, Budapest, Hungary.