460-4142/01 – Network Science II (MAS II)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantordoc. Mgr. Miloš Kudělka, Ph.D.Subject version guarantordoc. Mgr. Miloš Kudělka, Ph.D.
Study levelundergraduate or graduateRequirementChoice-compulsory type A
Year2Semesterwinter
Study languageCzech
Year of introduction2022/2023Year of cancellation
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
ANL0005 Ing. Tomáš Anlauf
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 2+2
Part-time Graded credit 18+0

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

Subject codeAbbreviationTitleRequirement
460-4141 MAS I Network Science I Compulsory

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

Part-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 3
Mandatory attendence participation: Activity in seminars, implementation of a selected method, elaboration of an analytical task, written test.

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Conditions for subject completion and attendance at the exercises within ISP: Completion of all mandatory tasks within individually agreed deadlines.

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2024/2025 (N0613A140034) Computer Science AZD P Czech Ostrava 2 Choice-compulsory type A study plan
2024/2025 (N0613A140034) Computer Science AZD K Czech Ostrava 2 Choice-compulsory type A study plan
2023/2024 (N0613A140034) Computer Science AZD K Czech Ostrava 2 Choice-compulsory type A study plan
2023/2024 (N0613A140034) Computer Science AZD P Czech Ostrava 2 Choice-compulsory type A study plan
2022/2023 (N0613A140034) Computer Science AZD K Czech Ostrava 2 Choice-compulsory type A study plan
2022/2023 (N0613A140034) Computer Science AZD P Czech Ostrava 2 Choice-compulsory type A study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction



2023/2024 Winter