Graph theory and social media

1.

Subject title

Graph theory and social media

Теорија на графови и социјални мрежи

2.

Code

F23L3W161

3.

Study program

Примена на информациски технологии, Софтверско инженерство и информациски системи, Стручни студии за програмирање, Компјутерско инженерство, Интернет, мрежи и безбедност, Software engineering and information systems, Компјутерски науки, Компјутерско инженерство, Интернет, мрежи и безбедност, Информатичка едукација, Software engineering and information systems, Примена на информациски технологии, Софтверско инженерство и информациски системи, Компјутерски науки, Стручни студии за програмирање, 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)

Прв циклус

6.

Academic year / semester

3 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Марија Михова проф. д-р Соња Гиевска

9.

Prerequisites for enrollment

Алгоритми и податочни структури или Примена на алгоритми и податочни структури

10.

Subject goals and competencies:

The aim of the course is to introduce students to the most important aspects of graph theory, with a more detailed review of the theory relevant to social network analysis. Students will get acquainted with the concepts, components and organization of social networks, as well as technologies for their development, interaction and analysis.

11.

Subject content:

Lectures: 1. Graphs and their representation, oriented and unoriented graphs. Subgraphs, isomorphism in graphs, path, cycle 2. Trees, distance, BFS and DFS trees. 3. Connectivity, Euler and Hamiltonian paths. Bipartiteness and pairing. 4. Vertex and edge coloring, independent sets and cliques, planar graphs 5. Networks. Structure and models of networks. Strong and weak ties in networks. Positive and negative relationships. Symmetry, dichotomy, measures of central symmetry Metrics. 6. Network profile - basic network characteristics 7. Application of machine learning for social network analysis 8. Representation of nodes, connections, graphs 9. Extracting knowledge from social networks 10. Modeling users and communities. Antisocial behavior 11. Application of game theory in analysis of networks and behavior of individuals and communities 12. Personalization and user profiling. Case studies

12.

Learning methods:

Предавања со користење на презентации, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа.

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

30 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

45 hours

16.

Other forms of activities

16.1.

Project tasks

15 hours

16.2.

Independent tasks

15 hours

16.3.

Homework

75 hours

17.

Grading method

17.1.

Tests

10 points

17.2.

Seminar work / project (presentation: written and oral)

15 points

17.3.

Activities and learning

10 points

17.4.

Final exam

70 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 15.1 и 15.2

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