Complex Network Models FS26
Lecture slides and exercises
- Week 1 (Introduction): Slides, Exercises, Solutions
- Week 2 (Local perspective and Galton-Watson trees): Exercises, Solutions
- Week 3 (The global perspective: sprinkling): Exercises, Solutions
- Week 4 (Inhomogeneous Degree Distributions): Exercises, Solutions
- Week 5 (The Chung-Lu Model): Exercises, Solutions
- Week 6 (The Chung-Lu Model, continued): Exercises, Solutions
- Week 7 (Chung-Lu multigraph and the Configuration Model): Exercises, Solutions
- Week 8 (Routing and Clustering in Chung-Lu graphs): Exercises, Solutions
- Week 9 (Routing in the Watts-Strogatz and Kleinberg models): Exercises, Solutions
- Week 10 (The GIRG model): Exercises, Solutions
- Week 11 (The GIRG model, cont'd): Exercises, Solutions
Organisation
- The lectures will be recorded and made available via the ETH video portal.
- There will be no exercise classes, as the course is designed to have a lower workload than other courses the Institute of Theoretical Computer Science offers. However, weekly exercise sheets will be handed out for your practice with the material. Submission is not mandatory, but you are always welcome to send your solutions by email to Kostas for feedback.
- The exams will be oral and last 25 minutes, of which 5 are preparation time given some initial questions.
Content overview
Complex network models are random graphs that feature one or several properties observed in real-world networks (e.g., social networks, internet graph, www). Depending on the application, different properties are relevant, and different complex network models are useful. This course gives an overview over some relevant models and the properties they do and do not cover.
Erdös-Renyi random graphs
Chung-Lu graphs
Configuration model
Kleinberg model
Geometric inhomogenenous random graphs
Degree distribution
Giant component
Clustering coefficient
Small-world properties
Community structures
Strong and weak ties
More details can be found in the course's Script.