The theory of complex networks provides generic tools to model and analyse systems in a broad range of disciplines, including biology, sociology, economics, physics and information science. Processes such as the dynamics of epidemics, the diffusion of information in social networks, the interactions between species in ecosystems, the evolution of urban systems or the communication between neurons in our brains are all actively studied using dynamical models on complex networks. In these systems, the patterns of connections between the systems' components at the local and mesoscopic levels play a fundamental role in the global dynamics. Studying these patterns allows one to better understand and predict the behaviour of these complex systems.
This module aims at providing an introduction to this interdisciplinary field of research by integrating tools from graph theory, statistics and dynamical systems. Applications related to current problems related to biological, urban and social systems as well as the impact of digitalisation on society will serve as examples during the lecture.
The slides of the course are available in the Download section.
The main resource for this course is the book "Networks" by Mark Newman accessible from the UZH network here: Newman, M. (2018). Networks 2nd ed. Oxford University Press or from anywhere in the world with the UZH VPN.