460-4141/01 – Network Science I (MAS I)
| Gurantor department | Department of Computer Science | Credits | 4 |
| Subject guarantor | RNDr. Eliška Ochodková, Ph.D. | Subject version guarantor | RNDr. Eliška Ochodková, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Optional |
| Year | 1 | 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 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.
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.
Recommended literature:
[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.
Additional study materials
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
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
• Introduction to network data analysis. Basic concepts, representations of network data.
• Statistics for network analysis.
• Basic global and local properties (centralities, path-based properties)
• Basic global and local properties (structural properties)
• Network robustness
• Basic models - random graph, small world, preferential attachment
• Methods of network construction from vector data.
• Communities and network community structure
• Network models generating community structure
• Correlation in networks
• Sampling methods for network data
• 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).Introduction to network data analysis. Basic concepts, representations of network data.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction