460-4127/01 – Analysis of Network Data (ASD)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantordoc. Mgr. Miloš Kudělka, Ph.D.Subject version guarantordoc. Mgr. Miloš Kudělka, Ph.D.
Study levelundergraduate or graduateRequirementChoice-compulsory type B
Study languageCzech
Year of introduction2019/2020Year of cancellation2021/2022
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
KUD007 doc. Mgr. Miloš Kudělka, Ph.D.
OH140 RNDr. Eliška Ochodková, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Graded credit 2+2

Subject aims expressed by acquired skills and competences

Learning outcomes of the course unit The aim of the course is to acquire knowledge related to the methods of network data analysis, especially the approaches associated with the measurement of local, global and time-varying network properties, algorithms for network structural properties analysis, generative network models and network representation structures. Students will be able to understand the analyzed data, will be able to correctly interpret and evaluate the results and will be able to present and visualize the results by suitable methods.

Teaching methods



In the course, students will be acquainted with basic and advanced algorithms for network analysis and visualization. Lectures will be devoted to the theoretical description of individual algorithms for individual analytical tasks, so that students are able to decide for themselves when which method is suitable, what its assumptions, what is its principle and what outputs can be obtained with it. The exercises will then be used for practical experiments on suitable datasets, experimenting with tools for analyzing network data and for evaluating experimental results.

Compulsory literature:

1. Albert-László Barabási. Network science. Cambridge university press, 2016. ISBN 978-1107076266 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.

Recommended literature:

1. Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014. ISBN: 9780521766333. 2. Jure Leskovec, Anand Rajaraman, David Ullman, Mining of Massive Datasets, 2nd editions, Cambridge University Press, Novemeber 2014, ISBN: 9781107077232, On-line http://infolab.stanford.edu/~ullman/mmds/book.pdf [2014-09-12]

Way of continuous check of knowledge in the course of semester

Obtaining sufficient number of points for participation and activity in exercises (19-36 points). Processed and evaluated experiment from analysis of real or reference network data (10-20 points). Implementation of selected algorithm from lectures or similar range (12-24 points). Successful completion of credit test (10-20 points).


Other requirements

Additional requirements are not placed on the student.


Subject has no prerequisities.


Subject has no co-requisities.

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.

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester, validity until: 2021/2022 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Graded credit Graded credit 100  51 3
Mandatory attendence participation: Hundred percent participation in the labs is required except for a doctor-confirmed disease.

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Conditions for subject completion and attendance at the exercises within ISP:

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2021/2022 (N0688A140014) Industry 4.0 P Czech Ostrava 1 Choice-compulsory type B study plan
2020/2021 (N0688A140014) Industry 4.0 P Czech Ostrava 1 Choice-compulsory type B study plan
2019/2020 (N0688A140014) Industry 4.0 P Czech Ostrava 1 Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

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