Course Unit Code | 460-4072/01 |
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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 * | First Year |
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Semester when the Course Unit is delivered | Summer 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 | 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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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 |
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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 |
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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 |
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Required Reading: |
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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]
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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.
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Recommended Reading: |
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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]
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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.
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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 |