Multilayer Social Networks

 Multilayer Social Networks

Multilayer Social Networks

by Mark E. Dickison, M. Magnani and L. Rossi.
Cambridge University Press.

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. The book is intended to be an introduction to the topic, accessible to people with different backgrounds. To reach this objective, it has been written by a physicist, a computer scientist and a sociologist.

This book has been partly funded by MIUR FIRB grant RBFR107725.


MARK E. 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 postdoctoral fellow at Pennsylvania State University in its USP program, which supports the U.S. 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 a Senior Lecturer in database systems and data mining at Uppsala University, Sweden, and has previously held positions at the National Research Council (CNR), Italy, at the University of Bologna and at Aarhus University, Denmark. He has written around 1.5 Kg of papers and he has an h-index. He has received several awards, including a Funniest Presentation award and a Best Young Chess Player at a local tournament with two participants. Matteo's mission is to create, preserve, teach and apply knowledge in the service of humanity. Luckily enough, most of the humanity is not aware of Matteo's mission.

LUCA ROSSI is an 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, and 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, University, and Research).

Table of contents

List of Abbreviations
1 Moving Out of Flatland
1.1 Multiple Social Networks in Our Everyday Experience
1.2 An Introductory Example
1.3 Scope and Other Learning Resources
1.4 Outline of the Book
1.5 Acknowledgments
2 Representing Multilayer Social Networks
2.1 Terminology and Model
2.2 Related Models
2.3 Data Sets
3 Measuring Multilayer Social Networks
3.1 Four Main Approaches
3.2 Actor Measures
3.3 Layer Measures
4 Data Collection and Preprocessing
4.1 Issues in Data Collection
4.2 Network Simplification
5 Visualizing Multilayer Networks
5.1 Four Main Approaches
5.2 Visualizing Multilayer Network Metrics
5.3 Visualizing Multilayer Network Structures
5.4 Augmented Networks: Structure + Measures
5.5 Simplified Network Visualization
6 Community Detection
6.1 Methods Based on Simplification
6.2 Combination of Single-Layer Communities
6.3 Multilayer Modularity Optimization
6.4 Multiple Actor Types
6.5 Community Interpretation, Evaluation, and Description
7 Edge Patterns
7.1 Edge Prediction
7.2 Layer Associativity
8 Formation of Multilayer Social Networks
8.1 General Properties for Social Network Formation
8.2 Single-Layer Network Formation
8.3 Multilayer Properties
8.4 Multilayer Formation Models
9 Information and Behavior Diffusion
9.1 Diffusion in Networks
9.2 Modeling Information Spreading
9.3 Opinion Formation and Behavior Adaptation
10 Future Directions
10.1 New Models and Measures
10.2 Multilayer Network Visualization
10.3 Communities and Other Groups
10.4 Formation, Diffusion, and Temporal Processes
10.5 Big Open Data


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