Multilayer Social Networks

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).

'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

• 1. Moving out of flatland; Part I. Models and Measures: 2. Representing multilayer social networks; 3. Measuring multilayer social networks; Part II. Mining Multilayer Networks: 4. Data collection and preprocessing; 5. Visualizing multilayer networks; 6. Community detection; 7. Edge patterns; Part III. Dynamical Processes: 8. Formation of multilayer social networks; 9. Information and behavior diffusion; Part IV. Conclusion: 10. Future directions.