PhD course: Modelling and mining multilayer networks

A multilayer network is a network (or graph) wherenodes can be organised into sets, called layers, and the same node can belong to one or more of the layers. This allows us to model a wide range of systems of interconnected entities, for example social networks where different types of actors are connected through different types of ties ("working together", "being friend", etc.). After presenting the multilayer network model, this course focuses on the analysis of a specific type of multilayer network, known as multiplex, where there is only one type of actors but multiple types of connections. The course covers a selection of topics related to community detection, layer comparison methods, actor measures, data exploration, and network generation. For each topic, a quick presentation of the relevant theory and methods will be followed by a practical application on a real pedagogical dataset. The practical tasks can be performed in Python or R; basic knowledge of Python or R is useful but not a strict requirement. The course is organised over four consecutive days. The first two days, the lecturer presents the theory and the attendees try some practical exercises to get familiar with the terminology and the main methods (one 3-hour seminar per day). The third day the attendees work independently, by reading and preparing a presentation of some selected papers on multilayer networks. On the fourth day, the papers are presented and discussed.

DS5 - 2th floor, cube 41B
23/07/2024  (09:00-13:00)
24/07/2024  (09:00-13:00)

8h - 2 CFU

Please remember that the teams connection must only be used by students unable to go to the classroom.