Multiplex Networks
Short Course Description
Presenter
Matteo Magnani
Associate professor
InfoLab, Department of Information Technology Uppsala University (infolab.it.uu.se)
Short bio
Matteo Magnani is an associate professor (“docent”) at Uppsala University, and director of the recently established Uppsala Information Laboratory (http://infolab.it.uu.se). He has developed and applied analysis methods for multilayer social networks in the context of national and European projects (FIRN and H2020 funding schemes). Since 2011 Matteo Magnani has worked in the area of multilayer network science, and his results are described in his book “Multilayer Social Networks”, Cambridge University Press (2016), in several peer-reviewed publications, and implemented in the multinet library. He is also the recipient of a STINT initiation grant with TokyoTech on the topic of clustering temporal networks and of a NOS-HS grant for the establishment of a Nordic network on online disinformation. He pairs high-quality research with high-quality teaching, and has received the title of “Excellent teacher” (2016) and the UTN pedagogical prize (2013).
A multiplex network is a network where actors are connected through different types of edges, such as "working together", "friend", etc. These different types of connections are also known as layers.
Multiplex networks are one of the most popular network analysis models, have been object of a large amount of research and are practically useful. Research in this area is still very active and we are now having for the first time full-fledged software libraries covering the whole spectrum of multiplex network analysis, from generation models and visualization functions to centrality measures and mining algorithms.
The tutorial will introduce the R multinet library for the analysis of multiplex social networks, first published on CRAN at the beginning of 2017. It will consist of very brief theoretical presentations of major concepts (multiplex centrality measures, clustering algorithms, etc.) followed by practical tasks where the participants will use the library to apply the concepts to a pedagogical dataset. The main topics covered will be:
- visualization
- actor measures (degree, neighborhood)
- actor/layer measures (layer relevance)
- layer comparison methods
- community detection (generalized Louvain, LART, clique percolation)
- generative models
Part of the presented material is covered in the book "Multilayer Social Networks", Cambridge, 2016. The workshop includes methods developed in different fields by several different authors
Requirements
Knowledge of R and igraph is useful but not necessary: the course will mainly focus on the functions of the library. The participants will use their own laptops, and recent versions of R and RStudio must be installed in advance.
References
Dickison, M. E., Magnani, M., & Rossi, L. (2016). Multilayer Social Networks. Cambridge University Press
Further readings on multilayer networks
Mikko Kivelä, Alexandre Arenas, Marc Barthelemy, James P. Gleeson, Yamir Moreno, and Mason A. Porter. 2014. “Multilayer Networks.” Physics and Society. Journal of Complex Networks 2 (3).
Stefano Boccaletti, Ginestra Bianconi, Regino Criado, Charo I. Del Genio, Jesús Gómez-Gardeñes, Miguel Romance, Irene Sendiña-Nadal, Zhen Wang, and Massimiliano Zanin. 2014. “The Structure and Dynamics of Multilayer Networks.” Physics Reports 544 (1).
Ginestra Bianconi. Multilayer Networks: Structure and Function. Oxford University Press. 2018.
Reviews
(on diffusion/propagation) Mostafa Salehi, Rajesh Sharma, Moreno Marzolla, Matteo Magnani, Payam Siyari, and Danilo Montesi. 2015. “Spreading Processes in Multilayer Networks.” IEEE Transactions on Network Science and Engineering 2 (2): 65–83.
(on layer comparison) Brodka, P., Chmiel, A., Magnani, M., and Ragozini, G. 2018. “Quantifying layer similarity in multiplex networks: a systematic study“. Royal Society Open Science, 5(8).
(on clustering) Obaida Hanteer, Roberto Interdonato, Matteo Magnani, Luca Rossi and Andrea Tagarelli. “Community Detection in Multiplex Networks.” Forthcoming.
(on simplification) Roberto Interdonato, Matteo Magnani, Diego Perna, Andrea Tagarelli and Davide Vega. “A survey on methods for multilayer network simplification“. Forthcoming.