Johannes Lengler: Catalogue data in Spring Semester 2023 |
| Name | Prof. Dr. Johannes Lengler |
| Address | Informatik (Theoretische Inform.) ETH Zürich, OAT Z 14.1 Andreasstrasse 5 8092 Zürich SWITZERLAND |
| johannes.lengler@inf.ethz.ch | |
| Department | Computer Science |
| Relationship | Adjunct Professor |
| Number | Title | ECTS | Hours | Lecturers | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 252-4202-00L | Seminar in Theoretical Computer Science  | 2 credits | 2S | E. Welzl, B. Gärtner, M. Hoffmann, J. Lengler, A. Steger, D. Steurer, B. Sudakov | |||||||||||
| Abstract | Presentation of recent publications in theoretical computer science, including results by diploma, masters and doctoral candidates. | ||||||||||||||
| Learning objective | To get an overview of current research in the areas covered by the involved research groups. To present results from the literature. | ||||||||||||||
| Prerequisites / Notice | This seminar takes place as part of the joint research seminar of several theory groups. Intended participation is for students with excellent performance only. Formal restriction is: prior successful participation in a master level seminar in theoretical computer science. | ||||||||||||||
| 263-4509-00L | Complex Network Models | 5 credits | 2V + 2A | J. Lengler | |||||||||||
| 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. | ||||||||||||||
| Learning 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 | ||||||||||||||
| Lecture notes | The script is available in moodle or at https://as.inf.ethz.ch/people/members/lenglerj/CompNetScript.pdf It 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" | ||||||||||||||
| Prerequisites / Notice | The students must be familiar with the basics of graph theory and of probability theory (e.g. linearity of expectation, inequalities of Markov, Chebyshev, Chernoff). The course "Randomized Algorithms and Probabilistic Methods" is helpful, but not required. | ||||||||||||||
| Competencies |
| ||||||||||||||