|
Lecturer(s)
|
-
Vodák Rostislav, doc. RNDr. Ph.D.
|
|
Course content
|
1. Introduction to complex systems. Graphs, their examples, and why work with graph ensembles. 2. Random G(n,p) and G(n,m) graphs and their basic characteristics. 3. Giant and small components, and their properties. 4. Generating functions. 5. Configuration models and their properties. 6. Percolation on graphs. 7. Epidemics and the processes of their spread. The classical approach and their modeling on graphs.
|
|
Learning activities and teaching methods
|
Lecture, Demonstration
- Attendace
- 52 hours per semester
|
|
Learning outcomes
|
Understand the mathematics of complex networks and models of network processes.
Comprehension Comprehension of complex networks, ability to solve practical problems.
|
|
Prerequisites
|
Fundamentals of discrete mathematics and Python programming.
|
|
Assessment methods and criteria
|
Oral exam, Seminar Work
Credit: Active participation. Presentation of a solution to a problem. Oral exam.
|
|
Recommended literature
|
-
Newman, M. (2010). Networks. An Introduction. Oxford.
-
Reuven Cohen, Shlomo Havlin. (2010). Complex Networks: Structure, Robustness and Function.
-
Thurner, Stefan; Hanel, Rudolf; Klimek, Peter. (2018). Introduction to the theory of complex systems.
-
V. Latora, V. Nicosia, G. Russo, Complex Networks. (2017). Principles, Methods and Applications.
|