Advances in Network Clustering and Blockmodeling
Wiley Series in Computational and Quantitative Social Science
1. Auflage Februar 2020
432 Seiten, Hardcover
Wiley & Sons Ltd
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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.
1 Introduction 1
Patrick Doreian, Vladimir Batagelj, and Anuska Ferligoj
2 Bibliometric Analyses of the Network Clustering Literature 11
Vladimir Batagelj, Anuska Ferligoj, and Patrick Doreian
3 Clustering Approaches to Networks 65
Vladimir Batagelj
4 Different Approaches to Community Detection 105
Martin Rosvall, Jean-Charles Delvenne, Michael T. Schaub, and Renaud Lambiotte
5 Label Propagation for Clustering 121
Lovro Subelj
6 Blockmodeling of Valued Networks 151
Carl Nordlund and Ales ?iberna
7 Treating Missing Network Data Before Partitioning 189
Anja ?nidarsi , Patrick Doreian, and Anuska Ferligoj
8 Partitioning Signed Networks 225
Vincent Traag, Patrick Doreian, and Andrej Mrvar
9 Partitioning Multimode Networks 251
Martin G Everett and Stephen P Borgatti
10 Blockmodeling Linked Networks 267
Ales ?iberna
11 Bayesian Stochastic Blockmodeling 289
Tiago P. Peixoto
12 Structured Networks and Coarse-Grained Descriptions: A Dynamical Perspective 333
Michael T. Schaub, Jean-Charles Delvenne, Renaud Lambiotte, and Mauricio Barahona
13 Scientific Co-Authorship Networks 363
Marjan Cugmas, Anuska Ferligoj, and Luka Kronegger
14 Conclusions and Directions for Future Work 389
Patrick Doreian, Anuska Ferligoj, and Vladimir Batagelj
Topic Index 399
Person Index 407
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 Maruaic 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, Anuaka Ferligoj and Nataaa Kej??ar he co-authored the book Understanding Large Temporal Networks and Spatial Networks, Wiley, 2014.
Anuaka 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.
