Network Science for Business, Economics, Informatics and Social Sciences

(fall semester lecture)

"Complexity is a sign of intelligence. Simplicity is a sign of wisdom"

Med Jones


Network Science is an interdisciplinary field of research that has become synonym with the study of multiple complex systems that pervade social and economic systems. Network refer to representations of systems whose constituents are linked together because of social ties, information flow, economic relations, etc. Network modelling is a methodology with ample applications in modern data-intensive fields which has multiple applications in management, marketing, informatics, among multiple others.

The course covers a wide range of topics: it starts with an introduction to the basic concepts about networks; it then refers to the most important large-scale properties that real-world networks exhibit, and how these properties  can be modelled through simple approaches, which lead to the mechanisms underlying their emergence; then, it introduces network analytic techniques which shed light on the most important properties of empirical networks. Finally, a brief introduction to modelling of diffusion of technologies/opinions/rumours/epidemies are taught.

During the course, special emphasis is employed in introducing both: network analysis and visualisation tools. The course is highly interactive, so expect large self-study+exercise sessions that complement the theory part. All the lectures consist of a theoretical part… , then, the students must develop (in small groups and always supported by the instructors) the practical exercises themselves. This permits the students to gain direct experience and familiarity with the concepts taught and the techniques involved. In this participatory environment, multiple exercises and the creation of visualisations play an important role.

Prof. Dr Claudio J. Tessone


Target Audience:
Master students assigned to “Wahlpflichtbereich" BWL 4

Each fall semester


Advanced network science including both network theory and social network analysis.


Fundamental courses in statistics (e.g. Emprical Method MOEC0021). Solid R and/or Python programming skills (or the willingness to develop the knowledge PRIOR to the lecture) are a necessary requirement.

Assignments (one per session) + a final project (on which the students can work for up to three weeks) are the evaluation of the course.

Dates and Location:
Mondays 14:00 -17:00
20th September - 20th December 2021

location: TBA on VVZ

For questions, please contact

Further information:

Don’t forget to officially register yourself using the registration tools at the University of Zurich.

OLAT element: