Network science

1.

Subject title

Network science

Мрежна наука

2.

Code

IT-Z-03

3.

Study program

Internet Technologies and cyber security, Cloud Computing, Data science in computer science and engineering, IT management, Bioinformatics, Security, Cryptography and Coding, Еducation with ICT, Eco-informatics, Inteligent Systems, Computer Science, Software for embedded systems, Software Engineering, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Software Engineering, Statistics and Data Analytics, Statistics and Data Analytics,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Игор Мишковски ворн. проф. д-р Мирослав Мирчев

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

Networks are a basic tool for modeling complex social, informational, technological and biological systems. The course will teach the students in analysis and knowledge discovery from massive complex networks data. The students will get familiar with the modern tools for network analysis and machine learning in graphs, as well as the most popular network models that abstract the basic properties of real complex networks.

11.

Subject content:

Properties of real massive networks of different types and advanced models for their representation. Robustness and fragility of communication networks, food webs and financial markets. General analysis of topological influences on the operation of communication networks and detailed analysis of the Internet and WWW. Spread of information, influences, ideas, crashes and contagions in social and communication networks. Knowledge extraction from large networks, such as node classification, link prediction and community detection, and application of graph neural networks. Representational learning in graphs: embedding nodes, links and whole graphs. Knowledge graphs and multi-layer complex networks. Identification of functional modules in biological networks. Temporal analysis of complex networks.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, практични вежби, тимска работа, пример случаи, поканети предавачи, самостојна изработка на проектна задача и семинарска работа и електронско учење.

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

45 + 15 + 30 + 50 + 40 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

45 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

15 hours

16.

Other forms of activities

16.1.

Project tasks

50 hours

16.2.

Independent tasks

30 hours

16.3.

Homework

40 hours

17.

Grading method

17.1.

Tests

45 points

17.2.

Seminar work / project (presentation: written and oral)

50 points

17.3.

Activities and learning

10 points

17.4.

Final exam

0 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

Реализирани активности

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

Механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature