What it is about
You will learn models of random graphs and how they describe real networks. The focuses are Erdős–Rényi models, generalized random graphs, the configuration model and preferential-attachment models as well as proof techniques for the number of components, degree distribution and distances.
What you will be able to do
- know different models of random graphs and their suitability for real networks
- understand phase transition and behavior in subcritical, critical and supercritical phases for Erdős–Rényi graphs
- understand properties of the degree distribution in several models
- be able to apply standard techniques such as comparison with branching processes and martingales
What the module consists of
- VorlesungDelivery of theoretical foundations, proofs and examples
- ÜbungDeepening through exercises and solution patterns for self-control
Teaching method
- Vortrag (Lecture)Presentation of content with examples and discussion to motivate own analyses
- ÜbungsgruppenWorking on problem sheets to deepen understanding and check comprehension