| Course Unit Code | 460-4142/01 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Choice-compulsory type A |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | Second Year |
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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 | Czech |
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| Prerequisites and Co-Requisites | |
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| Prerequisities | Course Unit Code | Course Unit Title |
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| 460-4141 | Network Science I |
| Name of Lecturer(s) | Personal ID | Name |
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| 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 follows the Methods of Network Analysis I. Its first goal is to study the dynamics of networks, the development of network properties over time, and the study of phenomena that may occur during the network development. The second goal of the course is to introduce multilayer networks as a natural generalization of simple networks with a focus on their types, properties, models, development over time, and the application of methods of their analysis. After completing the course, the student will understand the principles that affect the properties of simple and multilayer networks that change over time, will be able to apply methods related to the analysis of these properties, and prototype implementation of selected methods. The student will also be able to use tools and libraries to analyze simple and multilayer networks' development over time and visualize it. After applying the methods of network analysis and their development, the student 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 dynamics, evolving networks.
2. Spreading phenomena
3. Temporal networks
4. Development of dynamic network properties
5. Link prediction methods
6. Platforms for working with large-scale social networks
7. Introduction to multilayer networks, multiplex, and multi-slice networks and their representations
8. Measurement of properties in multilayer networks (centralities and relevance)
9. Measurement of properties in multilayer networks (path-based properties and random walk processes)
10. Communities in multilayer networks
11. Models in multilayer networks
12. Spreading phenomena in multilayer networks
13. Visualization of multilayer networks
Seminars follow the lectured topics and focus on solving practical tasks. Experiments are performed on medium and large-scale reference and real-world 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.
[2] Dickison, M.E., Magnani, M., and Rossi, L. (2016). Multilayer social networks. Cambridge University Press.
[3] Bianconi, G. (2018). Multilayer networks: structure and function. Oxford University Press.
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[1] Barabási, L-A. (2016). Network science. Cambridge University Press, 2016.
[2] Dickison, M.E., Magnani, M., and Rossi, L. (2016). Multilayer social networks. Cambridge University Press.
[3] Bianconi, G. (2018). Multilayer networks: structure and function. Oxford University Press.
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| Recommended Reading: |
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| [1] Newman, M. (2010). Networks: An Introduction. Oxford University Press. |
| [1] Newman, M. (2010). Networks: An Introduction. Oxford University Press. |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Graded credit | Graded credit | 100 | 51 |