| Course Unit Code | 460-4141/02 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Optional |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | English |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| OH140 | RNDr. Eliška Ochodková, Ph.D. |
| KUD007 | doc. Mgr. Miloš Kudělka, Ph.D. |
| Summary |
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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.
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| Learning Outcomes of the Course Unit |
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| The course aims to introduce complex networks focusing on their types (social, communication, biological, etc.), properties, models, and methods of their analysis. After completing the course, the student will understand the principles that affect the properties of networks. will be able to apply methods related to the analysis of these properties and implement prototypes of selected methods and models. Furthermore, he will be able to use tools and libraries for analysis and visualization of networks, and after the application of network analysis methods will be able to assess the relevance of the results and find an understandable interpretation. |
| Course Contents |
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1. Introduction to network data analysis. Basic concepts, representations of network data.
2. Statistics for network analysis.
3. Basic global and local properties (centralities, path-based properties)
4. Basic global and local properties (structural properties)
5. Network robustness
6. Basic models - random graph, small world, preferential attachment
7. Methods of network construction from vector data.
8. Communities and network community structure
9. Network models generating community structure
10. Correlation in networks
11. Sampling methods for network data
12. Network visualization
Seminars follow the lectured topics and focus on solving practical tasks. Experiments are performed on small and medium-scale reference networks with prototyping implementations of selected methods and using tools and libraries (e.g., Gephi, libraries for R and Python).
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| Recommended or Required Reading |
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| Required Reading: |
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| [1] Barabási, L-A. (2016). Network science. Cambridge University Press, 2016. |
| [1] Barabási, L-A. (2016). Network science. Cambridge University Press, 2016. |
| Recommended Reading: |
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[1] Zaki, M. J., Meira Jr, W. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press.
[2] Newman, M. (2010). Networks: An Introduction. Oxford University Press.
[3] Leskovec, J., Rajaraman, A., Ullman, J. D. (2014). Mining of massive datasets. Cambridge University Press.
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[1] Zaki, M. J., Meira Jr, W. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press.
[2] Newman, M. (2010). Networks: An Introduction. Oxford University Press.
[3] Leskovec, J., Rajaraman, A., Ullman, J. D. (2014). Mining of massive datasets. Cambridge University Press.
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| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Tasks are not Defined |