Introduction to Network Science (PhD level)

Period: Spring 2026

Head teachers: Christian Rohner, Davide Vega D’Aurelio

Registration open: 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 use the registration form . Please register latest May 13, 2026.

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.

Time plan

The schedule for the course is the following:

PART I: Networks: basic concepts (lectures with practical activities)

  • Mon, May 18, 09:00-9:30 Room XXXXX (Ångström laboratory) Introduction to the course (Davide Vega)
  • Mon, May 18, 9:30-12:00 Room XXXXX (Ångström laboratory) Basics: Models and measures (Matteo Magnani)
  • Mon, May 18, 13:00-15:30 Room XXXXX (Ångström laboratory) Basics: Models and measures (Matteo Magnani)
  • Tue, May 19, 09:00-10:30 Room XXXXX (Ångström laboratory) Propagation (Christian Rohner)
  • Tue, May 19, 10:30-12:00 Room XXXXX (Ångström laboratory) Modularity-based graph clustering (Fiona Skerman, Dept. of mathematics)
  • Tue, May 19, Tue, 13:00-14:00 Room XXXXX (Ångström laboratory) Temporal Networks (Christian Rohner)
  • Tue, May 19, Tue, 14:00-16:00 Room XXXXX (Ångström laboratory) Node distances and measures (Akrati Saxena, Leiden University) - we are trying to move this to Monday 1st

PART II: Selected topics (lectures with practical activities and guest lectures)

  • Mon, May 25, 09:00-12:00 Room XXXXX (Ångström laboratory) Role and positional analysis (Davide Vega, Uppsala University)
  • Mon, May 25, 13:15-15:15 Room XXXXX (Ångström laboratory) Multilayer networks (Georgios Panayiotou, Uppsala University)
  • Tue, May 26, 10:00-12:00 Room XXXXX (Ångström laboratory) Inference in networks (Martina Contisciani, Central European University)
  • Tue, May 26, 13:15-15:15 Room XXXXX (Ångström laboratory) Higher order networks (Hanlin Sun, NORDITA - Stockholm University and KTH Royal Institute of Technology)
  • Mon, June 1, 10:00-12:00 Room XXXXX (Ångström laboratory) Network visualization (Kostiantyn Kucher, Linköping University)
  • Mon, June 15, 09:00-12:00 Room XXXXX (Ångström laboratory) Program committee meeting and course conclusion

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 27.
  • Bidding deadline: Fri, May 29.
  • Paper assignment: Mon, June 3.
  • Deadline to submit reviews: Thu, June 11.
  • Program committee meeting and course conclusion: Mon, June 15 Room XXXXX (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).

Registration

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 use the registration form . Please register latest May 15, 2024.