Introduction to Network Science (PhD level)
Period: Spring 2022
Head teachers: Matteo Magnani, Christian Rohner, Davide Vega D’Aurelio
Description of the course
Network Science is a very active and interdisciplinary field aimed at studying physical, social and biological systems that can be modeled as sets of interconnected entities. The course covers the basics of network science, including social network analysis centrality measures (degree, betweenness, …), network properties (degree distribution, average path length, …), network models (Erdös-Renyi, small-world, preferential attachment), propagation (SI/SIR/SIS models, …), the main network mining tasks (position/role detection, link prediction, community detection) as well as recent developments (multilayer networks, etc.). These topics are presented both in theory, through lectures, and practically, where participants learn network analysis software libraries. Then, students choose and review a set of important papers from different areas, selected by the teachers based on their importance in the field, to learn about advanced applications and developments of the students’ interests. The basic topics will be introduced in theory and practice, using
R and different network analysis packages.
The schedule for the course is the following:
PART I: Networks: basic concepts (lectures with practical activities)
- Mon, May 23, 09:00-10:30 Room 101125 (Ångström laboratory) Introduction to the course (Matteo Magnani)
- Mon, May 23, 10:30-15:30 Room 101125 (Ångström laboratory) Basics: Models and measures (Matteo Magnani)
- Tue, May 24, 09:00-10:30 Room 101125 (Ångström laboratory) Introduction to graph mining (Matteo Magnani)
- Tue, May 24, 10:30-12:00 Room 101125 (Ångström laboratory) Modularity-based graph clustering (Fiona Skerman, Dept. of mathematics)
- Tue, May 24, Tue, 13:15-16:00 Room 101125 (Ångström laboratory) Propagation (Christian Rohner)
PART II: Selected topics (lectures with practical activities and guest lectures)
- Mon, May 30, 09:00-12:00 Room 101260 (Ångström laboratory) Roles and positions (Davide Vega)
- Mon, May 30, 13:15-15:15 Room 101260 (Ångström laboratory) Inferential community detection and network reconstruction (Tiago de Paula Peixoto, CEU Vienna, Austria)
- Tue, May 31, 09:00-12:00 Room 101260 (Ångström laboratory) Multiplex networks (Matteo Magnani)
- Tue, May 31, 13:15-15:00 Room 101260 (Ångström laboratory) Brain Networks (Onerva Kohronen, Aalto University, Finland / CTB, Universidad Politécnica de Madrid)
- Fri, June 10, 09:00- Room 101258 (Ångström laboratory) Network visualisation (Mohammad Gronien, LIST, Luxembourg)
PART III: Literature study
Students will bid and be assigned to relevant research papers covering various advanced topics, from a list selected by the teachers. The papers will be then discussed in groups.
- Bidding opens: Wed, May 25.
- Bidding deadline: Fri, May 27.
- Paper assignment: Tue, May 31.
- Deadline to submit reviews: Wed, June 08.
- Program committee meeting and course conclusion: Fri, June 10 Room 101258 (Ångström laboratory).
Credits and assessment
Students must actively participate in all the course activities. The course corresponds to 3 credits, or 2 weeks of full time work. While the course is open to people who are not PhD students, only PhD students can get credits.
Expected level and prerequisites
The course is targeted to PhD students willing to apply network science in their own discipline, but also experienced researchers with a consolidated research background in different areas. Being an interdisciplinary course intended for a broad audience, the topics will be presented in a self-contained way, giving to the students the chance to focus on topics of their interest during the last part of the course. However, the presentation will be at an advanced level (in terms of speed of presentation and content density).
The course is free and open to everyone upon registration, the number of available places is limited; PhD students will be given priority. If interested, please send an email to firstname.lastname@example.org specifying your name, department, research field and one sentence motivating your interest. Please register latest April 15, 2022.