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Data Analysis II

* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code460-4072/01
Number of ECTS Credits Allocated4 ECTS credits
Type of Course Unit *Optional
Level of Course Unit *Second Cycle
Year of Study *First Year
Semester when the Course Unit is deliveredSummer Semester
Mode of DeliveryFace-to-face
Language of InstructionCzech
Prerequisites and Co-Requisites Course succeeds to compulsory courses of previous semester
Name of Lecturer(s)Personal IDName
OH140RNDr. Eliška Ochodková, Ph.D.
KUD007doc. Mgr. Miloš Kudělka, Ph.D.
Summary
Students will be introduced to advanced algorithms for analysis and visualization of networks. The lectures will deal with the theoretical description of the individual algorithms for the different analytical tasks so that the students will be able to decide which methods are suitable, what is their theoretical background and what outputs can be obtained. The seminars will then serve for practical experiments on suitable datasets, experimenting with tools for analyzing network data and for evaluating experimental results.
Learning Outcomes of the Course Unit
The goal of the subject is to acquire knowledge related to advanced methods of network data analysis, especially with the approaches connected with measurement of community and time changing properties of networks, algorithms for analysis of structural properties of networks and generative models of networks. Students will be able to understand the analyzed data, interpret and evaluate the achieved results correctly and present and visualize the results with suitable methods.
Course Contents
Lectures:
1. Network construction from vector data
2. Network clustering I, matrix algorithms
3. Network clustering II, graph partitioning (Kernighan-Lin)
4. Network sampling
5. Advanced network models I, generating of community structure
6. Advanced network models II, evolving networks
7. Community detection
8. Modularity and community structure
9. Correlation in networks
10. Network resilience and spread phenomena
11. Temporal networks
12. Multilayer networks I, properties and measures
13. Multilayer networks II, random walks and projections
14. Visualization of network data

Seminars are directly connected to the lectures, discussions and knowledge verification using experiments on data sets.
Recommended or Required Reading
Required Reading:
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]
1. Albert-László Barabási. Network science. Cambridge university press, 2016. ISBN 978-1107076266
2. Mark Newman. Networks: An Introduction. Oxford University Press, 2010.
3. Mark E. Dickison, Matteo Magnani, and Luca Rossi. Multilayer social networks. Cambridge University Press, 2016.
Recommended Reading:
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]
1. Albert-László Barabási. Network science. Cambridge university press, 2016. ISBN 978-1107076266
2. Mark Newman. Networks: An Introduction. Oxford University Press, 2010.
3. Mark E. Dickison, Matteo Magnani, and Luca Rossi. Multilayer social networks. Cambridge University Press, 2016.
Planned learning activities and teaching methods
Lectures, Tutorials
Assesment methods and criteria
Task TitleTask TypeMaximum Number of Points
(Act. for Subtasks)
Minimum Number of Points for Task Passing
Graded creditGraded credit100 51