Advances in Network Clustering and Blockmodeling

Advances in Network Clustering and Blockmodeling

by Patrick Doreian, Vladimir Batagelj, Anuska Ferligoj
Released February 2020
Publisher(s): Wiley
ISBN: 9781119224709

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Book description

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.

Table of contents

  1. Cover
  2. List of Contributors
  3. 1 Introduction
    1. 1.1 On the Chapters
    2. 1.2 Looking Forward
  4. 2 Bibliometric Analyses of the Network Clustering Literature
    1. 2.1 Introduction
    2. 2.2 Data Collection and Cleaning
    3. 2.3 Analyses of the Citation Networks
    4. 2.4 Link Islands in the Clustering Network Literature
    5. 2.5 Authors
    6. 2.6 Summary and Future Work
    7. Acknowledgements
    8. References
    9. Notes
  5. 3 Clustering Approaches to Networks
    1. 3.1 Introduction
    2. 3.2 Clustering
    3. 3.3 Approaches to Clustering
    4. 3.4 Clustering Graphs and Networks
    5. 3.5 Clustering in Graphs and Networks
    6. 3.6 Agglomerative Method for Relational Constraints
    7. 3.7 Some Examples
    8. 3.8 Conclusion
    9. Acknowledgements
    10. References
  6. 4 Different Approaches to Community Detection
    1. 4.1 Introduction
    2. 4.2 Minimizing Constraint Violations: the Cut-based Perspective
    3. 4.3 Maximizing Internal Density: the Clustering Perspective
    4. 4.4 Identifying Structural Equivalence: the Stochastic Block Model Perspective
    5. 4.5 Identifying Coarse-grained Descriptions: the Dynamical Perspective
    6. 4.6 Discussion
    7. 4.7 Conclusions
    8. Acknowledgements
    9. References
    10. Note
  7. 5 Label Propagation for Clustering
    1. 5.1 Label Propagation Method
    2. 5.2 Label Propagation as Optimization
    3. 5.3 Advances of Label Propagation
    4. 5.4 Extensions to Other Networks
    5. 5.5 Alternative Types of Network Structures
    6. 5.6 Applications of Label Propagation
    7. 5.7 Summary and Outlook
    8. References
    9. Notes
  8. 6 Blockmodeling of Valued Networks
    1. 6.1 Introduction
    2. 6.2 Valued Data Types
    3. 6.3 Transformations
    4. 6.4 Indirect Clustering Approaches
    5. 6.5 Direct Approaches
    6. 6.6 On the Selection of Suitable Approaches
    7. 6.7 Examples
    8. 6.8 Conclusion
    9. Acknowledgements
    10. References
    11. Notes
  9. 7 Treating Missing Network Data Before Partitioning
    1. 7.1 Introduction
    2. 7.2 Types of Missing Network Data
    3. 7.3 Treatments of Missing Data (Due to Actor Non-Response)
    4. 7.4 A Study Design Examining the Impact of Non-Response Treatments on Clustering Results
    5. 7.5 Results
    6. 7.6 Conclusions
    7. Acknowledgements
    8. References
    9. Notes
  10. 8 Partitioning Signed Networks
    1. 8.1 Notation
    2. 8.2 Structural Balance Theory
    3. 8.3 Partitioning
    4. 8.4 Empirical Analysis
    5. 8.5 Summary and Future Work
    6. References
    7. Note
  11. 9 Partitioning Multimode Networks
    1. 9.1 Introduction
    2. 9.2 Two-Mode Partitioning
    3. 9.3 Community Detection
    4. 9.4 Dual Projection
    5. 9.5 Signed Two-Mode Networks
    6. 9.6 Spectral Methods
    7. 9.7 Clustering
    8. 9.8 More Complex Data
    9. 9.9 Conclusion
    10. References
  12. 10 Blockmodeling Linked Networks
    1. 10.1 Introduction
    2. 10.2 Blockmodeling Linked Networks
    3. 10.3 Examples
    4. 10.4 Conclusion
    5. Acknowledgements
    6. References
    7. Notes
  13. 11 Bayesian Stochastic Blockmodeling
    1. 11.1 Introduction
    2. 11.2 Structure Versus Randomness in Networks
    3. 11.3 The Stochastic Blockmodel
    4. 11.4 Bayesian Inference: The Posterior Probability of Partitions
    5. 11.5 Microcanonical Models and the Minimum Description Length Principle
    6. 11.6 The “Resolution Limit” Underfitting Problem and the Nested SBM
    7. 11.7 Model Variations
    8. 11.8 Efficient Inference Using Markov Chain Monte Carlo
    9. 11.9 To Sample or To Optimize?
    10. 11.10 Generalization and Prediction
    11. 11.11 Fundamental Limits of Inference: The Detectability–Indetectability Phase Transition
    12. 11.12 Conclusion
    13. References
    14. Notes
  14. 12 Structured Networks and Coarse-Grained Descriptions: A Dynamical Perspective
    1. 12.1 Introduction
    2. 12.2 Part I: Dynamics on and of Networks
    3. 12.3 Part II: The Influence of Graph Structure on Network Dynamics
    4. 12.4 Part III: Using Dynamical Processes to Reveal Network Structure
    5. 12.5 Discussion
    6. Acknowledgements
    7. References
  15. 13 Scientific Co-Authorship Networks
    1. 13.1 Introduction
    2. 13.2 Methods
    3. 13.3 The Data
    4. 13.4 The Structure of Obtained Blockmodels
    5. 13.5 Stability of the Obtained Blockmodel Structures
    6. 13.6 Conclusions
    7. Acknowledgements
    8. References
    9. Notes
  16. 14 Conclusions and Directions for Future Work
    1. 14.1 Issues Raised within Chapters
    2. 14.2 Linking Ideas Found in Different Chapters
    3. 14.3 A Brief Summary and Conclusion
    4. References
  17. TOPIC INDEX
  18. PERSON INDEX
  19. End User License Agreement

Product information

  • Title: Advances in Network Clustering and Blockmodeling
  • Author(s): Patrick Doreian, Vladimir Batagelj, Anuska Ferligoj
  • Release date: February 2020
  • Publisher(s): Wiley
  • ISBN: 9781119224709