IV124 Complex Networks

Faculty of Informatics
Spring 2022
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
Taught in person.
doc. RNDr. Eva Hladká, Ph.D. (lecturer), RNDr. Jan Fousek, Ph.D. (deputy)
Ing. Eva Výtvarová (lecturer)
Guaranteed by
doc. RNDr. Eva Hladká, Ph.D.
Department of Computer Systems and Communications - Faculty of Informatics
Supplier department: Department of Computer Systems and Communications - Faculty of Informatics
Prerequisites (in Czech)
Doporučenými předměty jsou IV111 Pravděpodobnost v informatice a MV011 Statistika I, které usnadní studentům pochopení pravděpodobnostních modelů za náhodnými grafy a statistické části analýzy reálných sítí. Předpokládá se základní znalost teorie grafů.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 38 fields of study the course is directly associated with, display
Course objectives
Many complex systems can be viewed as a network of interacting units. This view helps to study and understand important phenomena present in systems such as human brain, internet, economy, social groups and others. The prezence of big data stimulated development of fundamental theories to describe and analyse complex networks in many fields. Course includes tutorials where real data sets will be analysed.
Learning outcomes
The students will be able to define the local and global network topological measures, and will be aware of the algorithms which can be used for their computation. They will be ready to explain the principles of generating random graphs with given structural properties and to understand their role in data analysis. The students will be able to apply the complex network analysis to empirical data sets across the application domains. Given raw data, they will be also capable of designing a network-oriented analysis, formulate relevant hypothesis and interpret correctly the results.
  • Intro
  • Random graphs
  • Central nodes
  • COmunity structure
  • Application of centrality and modularity
  • Power law I
  • power-law II
  • Small-world networks
  • Random walks
  • Robustness and stability
  • Social-economic networks
  • Internet as a complex network
  • Biologic networks
  • Visualisation
    recommended literature
  • PELÁNEK, Radek. Modelování a simulace komplexních systémů. Jak lépe porozumět světu (Modeling and simulation of complex systems). 1st ed. Brno: Masarykova univerzita, 2012. 236 pp. e-book. ISBN 978-80-210-5807-1. info
  • NEWMAN, M. E. J. Networks : an introduction. Oxford: Oxford University Press, 2010. xi, 772. ISBN 9780199206650. info
  • CSERMELY, Péter. Weak links : stabilizers of complex systems from proteins to social networks. 1st ed. Berlin: Springer, 2006. xix, 392. ISBN 3540311513. info
  • BARABÁSI, Albert-László. V pavučině sítí. Translated by František Slanina. Vyd. 1. V Praze: Paseka, 2005. 274 s. ISBN 8071857513. info
Teaching methods
Lectures, tutorial, home works.
Assessment methods
40 % project, 20 % homework, 40 % final exam
Language of instruction
Further Comments
The course is taught once in two years.
The course is taught: every week.
The course is also listed under the following terms Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020.
  • Enrolment Statistics (Spring 2022, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2022/IV124