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Network Science I

Summary

Lectures are focused on the theoretical background of properties, models, and analytical methods so that students are able to decide what purpose the particular methods are suitable for, how to set and apply them, what outcomes can be obtained through their application and how these outcomes can be interpreted.
Seminars are focused on experiments with suitable data sets, implementations of method prototypes, experimenting with tools and libraries for analysis and visualization of network data, and evaluating the experiments' results.

After completing the course, students will understand the principles that influence network properties, be able to apply methods related to the analysis of these properties, and be able to prototype selected methods and models. They will also be able to use tools and libraries for network analysis and visualization, and after applying network analysis methods, they will be able to assess the relevance of the results and find a comprehensible interpretation of them.

Literature

[1] Barabási, L-A. (2016). Network science. Cambridge University Press, 2016.
[2] Newman, M. (2010). Networks: An Introduction. Oxford University Press.

Advised literature

[1] Zaki, M. J., Meira Jr, W. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press.
[2] Leskovec, J., Rajaraman, A., Ullman, J. D. (2014). Mining of massive datasets. Cambridge University Press.
[3] Eric D. Kolaczyk , Gábor Csárdi. Statistical Analysis of Network Data with R. 2020. https://link.springer.com/book/10.1007/978-1-4939-0983-4
[4] Dmitry Zinoviev. Complex Network Analysis in Python. 2018. O'Reilly. ISBN 9781680505399 


Language of instruction čeština, angličtina
Code 460-4141
Abbreviation MAS1
Course title Network Science I
Coordinating department Department of Computer Science
Course coordinator RNDr. Eliška Ochodková, Ph.D.