460-4142/01 – Network Science II (MAS II)
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 A |
Year | 2 | Semester | winter |
| | Study language | Czech |
Year of introduction | 2022/2023 | Year of cancellation | |
Intended for the faculties | FEI | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
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.
Teaching methods
Lectures
Tutorials
Summary
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.
Compulsory literature:
[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.
Recommended literature:
[1] Newman, M. (2010). Networks: An Introduction. Oxford University Press.
Way of continuous check of knowledge in the course of semester
Credit is awarded for at least a minimum number of points, including points for activity in seminars, two semestral tasks (one is a more extensive implementation of the selected method, and the other is a complex analytical task), and a written test. The activity in the seminars includes a balanced share of prototyping implementations of selected methods and the implementation of analytical tasks using libraries for analysis and visualization of networks in a scripting language (R, Python). The written test takes the form of open-ended questions related to the topics covered in the lectures.
E-learning
Other requirements
No other requirements.
Prerequisities
Co-requisities
Subject has no co-requisities.
Subject syllabus:
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).
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction