460-4127/01 – Analysis of Network Data (ASD)
| Gurantor department | Department of Computer Science | Credits | 4 |
| Subject guarantor | doc. Mgr. Miloš Kudělka, Ph.D. | Subject version guarantor | doc. Mgr. Miloš Kudělka, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Choice-compulsory type B |
| Year | 1 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2019/2020 | Year of cancellation | 2021/2022 |
| Intended for the faculties | FEI | Intended for study types | Follow-up Master |
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
Lectures
Tutorials
Summary
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:
Recommended literature:
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).
E-learning
Other requirements
Additional requirements are not placed on the student.
Prerequisities
Subject has no prerequisities.
Co-requisities
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
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.