Multilayer networks, in particular multilayer social networks, where users belong to and interact on different networks at the same time, are an active research area in social network analysis, computer science, and physics. These networks have traditionally been studied within these separate research communities, leading to the development of several independent models and methods to deal with the same set of problems. This book unifies and consolidates existing practical and theoretical knowledge on multilayer networks including data collection and analysis, modeling, and mining of multilayer social network systems, the evolution of interconnected social networks, and dynamic processes such as information spreading. A single real dataset is used to illustrate the concepts presented throughout the book, demonstrating both the practical utility and the potential shortcomings of the various methods. Researchers from all areas of network analysis will learn new aspects and future directions of this emerging field.
Arvustused
'A well-crafted and clear exposition of the important area of multilayer social networks. The authors skillfully entwine theory and applications to produce a highly readable account of recent research in this ever expanding field. A must have for any network scientist.' Martin Everett, University of Manchester 'A wonderful compendium of methods for multivariate - multirelational, multimodal, multiplex, etc. - networks, focusing on extensions of traditional techniques (subgroups, centrality, clustering, and visualizing). Buy this book and use it! Cambridge University Press remains at the forefront of publishing network science books.' Stanley Wasserman, Indiana University, Bloomington, and National Research University Higher School of Economics, Moscow 'This is a comprehensive guide to a fascinating mathematical and computational perspective on real-world social phenomena Overall, the book provides a thorough introduction to multilayer social networks, followed by an extensive literature review. The intensive interest and the enthusiasm of the authors for this area are contagious and stimulate the readers to further explore multilayer networks as tools for their own research domains. Hence, the book is recommended to researchers, practitioners, and teachers who are eager to 'escape from Flatland' and investigate new dimensions.' Lefteris Angelis, Computing Reviews
Muu info
This book unifies and consolidates methods for analyzing multilayer networks arising from the social and physical sciences and computing.
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ix | |
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1 | (14) |
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1.1 Multiple Social Networks in Our Everyday Experience |
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1 | (4) |
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1.2 An Introductory Example |
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5 | (2) |
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1.3 Scope and Other Learning Resources |
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7 | (1) |
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8 | (3) |
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11 | (4) |
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PART I MODELS AND MEASURES |
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2 Representing Multilayer Social Networks |
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15 | (23) |
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2.1 Terminology and Model |
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17 | (3) |
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20 | (12) |
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32 | (6) |
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3 Measuring Multilayer Social Networks |
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38 | (29) |
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39 | (3) |
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42 | (19) |
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61 | (6) |
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PART II MINING MULTILAYER NETWORKS |
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4 Data Collection and Preprocessing |
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67 | (12) |
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4.1 Issues in Data Collection |
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68 | (5) |
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4.2 Network Simplification |
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73 | (6) |
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5 Visualizing Multilayer Networks |
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79 | (17) |
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79 | (3) |
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5.2 Visualizing Multilayer Network Metrics |
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82 | (3) |
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5.3 Visualizing Multilayer Network Structures |
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85 | (5) |
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5.4 Augmented Networks: Structure + Measures |
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90 | (2) |
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5.5 Simplified Network Visualization |
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92 | (4) |
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96 | (23) |
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6.1 Methods Based on Simplification |
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99 | (5) |
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6.2 Combination of Single-Layer Communities |
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104 | (4) |
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6.3 Multilayer Modularity Optimization |
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108 | (5) |
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113 | (1) |
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6.5 Community Interpretation, Evaluation, and Description |
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113 | (6) |
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119 | (14) |
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120 | (5) |
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125 | (8) |
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PART III DYNAMICAL PROCESSES |
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8 Formation of Multilayer Social Networks |
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133 | (16) |
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8.1 General Properties for Social Network Formation |
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134 | (1) |
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8.2 Single-Layer Network Formation |
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135 | (5) |
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8.3 Multilayer Properties |
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140 | (3) |
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8.4 Multilayer Formation Models |
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143 | (6) |
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9 Information and Behavior Diffusion |
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149 | (20) |
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9.1 Diffusion in Networks |
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149 | (2) |
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9.2 Modeling Information Spreading |
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151 | (10) |
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9.3 Opinion Formation and Behavior Adaptation |
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161 | (8) |
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169 | (10) |
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10.1 New Models and Measures |
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170 | (2) |
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10.2 Multilayer Network Visualization |
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172 | (1) |
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10.3 Communities and Other Groups |
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173 | (2) |
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10.4 Formation, Diffusion, and Temporal Processes |
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175 | (1) |
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176 | (3) |
| Glossary |
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179 | (4) |
| Bibliography |
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183 | (18) |
| Index |
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201 | |
Mark Dickison is a Data Science Manager at Capital One, where he attempts to put his knowledge of complex systems and technical skills at the forefront of solving business problems while still finding time to stay current with theory. He has been a post-doctoral fellow at Pennsylvania State in their USP program, which supports the US Defense Threat Reduction Agency, one of the first organizations to focus on multiple network models. His research interests fall within multidisciplinary network modeling, including network formation, and epidemiological and opinion spreading, as well as data mining and machine learning. Matteo Magnani is Senior Lecturer in database systems and data mining at Uppsala University, and has previously held positions at CNR, Italy, at the University of Bologna and at Aarhus University. He authored one of the first research papers on multilayer social networks (best paper award at the ASONAM conference), and organized multiple conference tracks (at SunBelt, NetSci) as well as a journal special issue on this topic. Luca Rossi is Assistant Professor in the Communication and Culture research group of the IT University of Copenhagen. His research connects traditional sociological approaches with computational approaches. He has presented his work at many international conferences, including: IR, SBP, ASONAM, SunBelt, ICWSM. He has teaching experience at both undergraduate and graduate levels, and has successfully attracted funding on complex social network analysis from PRIN and FIRB schemes (Italian Ministry for education).
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