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Lecturer
Simone Baldassarri simone.baldassarri@gssi.it
Timetable and workload
Lectures: 8 hours
Course content
Describing the evolution of dynamic networks together with dynamic processes running on them constitutes a major challenge in network science. Despite considerable efforts in past years, and notable progress on an intuitive and approximative level, our mathematical understanding of such systems is still in its infancy. This mini-course focuses on a class of models where this challenge can be addressed in a mathematically controlled way: interacting particle systems evolving on dense dynamic random graphs. Using the voter model and the SIR epidemic model as guiding examples, we will explore how opinions and infections co-evolve with an underlying network that changes over time. After reviewing recent rigorous results in this direction, the course will present new contributions on the macroscopic behavior of these systems, highlighting the role of network dynamics, feedback mechanisms, and emergent collective phenomena.