Networks are all around us, shaping phenomena like epidemics, communication, and transportation. In this talk, we will explore how real-world problems can be analyzed and solved using graph-based methods and simple algorithms. Drawing from examples such as trade networks, corporate structures, and historical data, I will demonstrate how network analysis reveals insights that would otherwise remain hidden. Using NetworKit (and NetworkX), we will analyze real-world datasets to answer questions like:

1. What does the core-periphery model reveal about trade networks?
2. Could we have predicted that Moscow will become Russia's capital?
3. How do corporate hierarchies differ from interaction hierarchies within organizations?

Throughout the talk, I will introduce key concepts in network analysis and showcase Python as a tool for research. Attendees will have access to all datasets and code, enabling them to replicate the analyses and apply these techniques to their own projects. This session is designed for Python enthusiasts with an interest in data science, networks, and/or applied research.