Delving deep into the intricacies of complex systems—be they social, biological, or technological—is vital for accurately modeling and effectively tackling the pressing issues that define our world today. Take, for instance, the need to diminish polarization and radicalization in online discussions, predict high-frequency financial transactions or understand the dynamic interactions among proteins.
Building upon the theoretical groundwork laid in "Social Network Analysis," this laboratory course aims to equip students with the tools to construct robust pipelines for analyzing complex systems derived from real-world data and represented as graphs. It will introduce key methodologies for data collection and preprocessing for graph analysis, delve into models designed to enhance the graph's descriptive capabilities and practical applications and explore techniques for evaluating experimental outcomes. The course emphasizes hands-on learning, empowering students to gain practical insights into these concepts through the utilization of advanced Python libraries.
- Teacher: BARBARA GUIDI
- Teacher: GIULIO ROSSETTI