Terminated in academic year 2021/2022

Analysis of Network Data

Type of study	Follow-up Master
Language of instruction	Czech
Code	460-4127/01
Abbreviation	ASD
Course title	Analysis of Network Data
Credits	4
Coordinating department	Department of Computer Science
Course coordinator	doc. Mgr. Miloš Kudělka, Ph.D.

Subject syllabus

Lectures:
1. Networks and their properties, types of networks and their representation.
2. Methods of measuring the importance of peaks in networks
3. Structure and global properties of large networks, basic network models
4. Basic data structures for network representation and network analysis algorithms
5. Clusters in networks, matrix algorithms. dividing graph.
6. Sampling
7. Models of networks with community structure
8. Networking models for evolving networks
9. Modularity and community structure, detection of networks in networks
10. Correlation in networks
11. Network resistance and propagation of phenomena
12. Temporal networks
13. Multilayer networks, properties and measures, random walks and projections.
14. Network visualization methods

Exercises at the computer lab are thematically related to lectures, practical demonstrations, discussions and experiments.

Literature

1. Albert-László Barabási. Network science. Cambridge university press, 2016.
2. Mark Newman. Networks: An Introduction. Oxford University Press, 2010. ISBN 978-0199206650.
3. Mark E. Dickison, Matteo Magnani, and Luca Rossi. Multilayer social networks. Cambridge University Press, 2016.

Advised literature

1. Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014. .
2. Jure Leskovec, Anand Rajaraman, David Ullman, Mining of Massive Datasets, 2nd editions, Cambridge University Press, Novemeber 2014, , On-line http://infolab.stanford.edu/~ullman/mmds/book.pdf [2014-09-12]

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