460-4141/02 – Network Science I (MAS I)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantorRNDr. Eliška Ochodková, Ph.D.Subject version guarantorRNDr. Eliška Ochodková, Ph.D.
Study levelundergraduate or graduateRequirementChoice-compulsory type A
Year1Semesterwinter
Study languageEnglish
Year of introduction2022/2023Year of cancellation
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
KUD007 doc. Mgr. Miloš Kudělka, Ph.D.
OH140 RNDr. Eliška Ochodková, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Graded credit 28+28

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.

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:

1. Introduction to network data analysis. Basic concepts, representations of network data. 2. Statistics for network analysis. 3. Basic global and local properties (centralities, path-based properties) 4. Basic global and local properties (structural properties) 5. Network robustness 6. Basic models - random graph, small world, preferential attachment 7. Methods of network construction from vector data. 8. Communities and network community structure 9. Network models generating community structure 10. Correlation in networks 11. Sampling methods for network data 12. 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).

Conditions for subject completion

Full-time form (validity from: 2022/2023 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Graded credit Graded credit 100  51 2
Mandatory attendence participation: Participation in the exercises is compulsory and is monitored. The scope of the compulsory participation will be communicated to the students by the course supervisor at the beginning of the semester.

Show history

Conditions for subject completion and attendance at the exercises within ISP: Completion of all mandatory tasks within individually agreed deadlines. The extent of participation in the exercises is agreed by the student with the course supervisor at the beginning of the semester.

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2024/2025 (N0613A140035) Computer Science AZD P English Ostrava 1 Choice-compulsory type A study plan
2024/2025 (N0688A140015) Industry 4.0 P English Ostrava 2 Choice-compulsory type B study plan
2024/2025 (N0541A170008) Computational and Applied Mathematics (S02) Computational Methods and HPC P English Ostrava 1 Optional study plan
2024/2025 (N0541A170008) Computational and Applied Mathematics (S01) Applied Mathematics P English Ostrava 1 Optional study plan
2023/2024 (N0688A140015) Industry 4.0 P English Ostrava 2 Choice-compulsory type B study plan
2023/2024 (N0613A140035) Computer Science AZD P English Ostrava 1 Choice-compulsory type A study plan
2023/2024 (N0541A170008) Computational and Applied Mathematics (S02) Computational Methods and HPC P English Ostrava 1 Optional study plan
2023/2024 (N0541A170008) Computational and Applied Mathematics (S01) Applied Mathematics P English Ostrava 1 Optional study plan
2022/2023 (N0613A140035) Computer Science AZD P English Ostrava 1 Choice-compulsory type A study plan
2022/2023 (N0688A140015) Industry 4.0 P English Ostrava 2 Choice-compulsory type B study plan
2022/2023 (N0541A170008) Computational and Applied Mathematics (S01) Applied Mathematics P English Ostrava 1 Optional study plan
2022/2023 (N0541A170008) Computational and Applied Mathematics (S02) Computational Methods and HPC P English Ostrava 1 Optional study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner
ECTS - mgr. 2024/2025 Full-time English Optional 401 - Study Office stu. block
V - ECTS - mgr. 2023/2024 Full-time English Optional 401 - Study Office stu. block
V - ECTS - mgr. 2022/2023 Full-time English Optional 401 - Study Office stu. block

Assessment of instruction



2023/2024 Winter