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Complex Network Models
Last Updated: 2026-02-05 16:23:15
Abstract
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.
Objective
The students get familiar with a portfolio of network models, and they know their features and shortcomings. For a given application, they can identify relevant properties for this applications and can select an appropriate network model.
Content
Network models: Erdös-Renyi random graphs, Chung-Lu graphs, configuration model, Kleinberg model, geometric inhomogeneous random graphs Properties: degree distribution, structure of giant and smaller components, clustering coefficient, small-world properties, community structures, weak ties
Resources
Lecture Notes
The script is available in moodle or athttps://as.inf.ethz.ch/people/members/lenglerj/CompNetScript.pdfIt will be updated during the semester.
Literature
Latora, Nikosia, Russo: "Complex Networks: Principles, Methods and Applications" van der Hofstad: "Random Graphs and Complex Networks. Volume 1"
General Information
- Language
- English
- Levels
- MSC , WBZ
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- oral 20 minutes
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Complex Network Models |
|
2 h weekly |
| independent project | Complex Network Models | No time listed | 2 h weekly |