Social Networks

Data is displayed for academic year: 2023./2024.

Course Description

The course offers students the opportunity to acquire knowledge and skills in the interdisciplinary field of social networks. Social networks are not only the most popular service based on the Internet infrastructure, but also a true global phenomenon that greatly affects the modern way of life and doing business. Students will acquire theoretical knowledge about the structure and processes in social networks, as well as practical knowledge and skills about application of social networks or achieving individual user or business goals.

Study Programmes

University graduate
[FER3-EN] Data Science - profile
Elective courses (1. semester)
Recommended elective courses (3. semester)

Learning Outcomes

  1. explain the role and importance of social networks in today's society
  2. distinguish between social networks with respect to their structural properties and processes that occur in them
  3. differentiate methods for analysis of social networks
  4. use appropriate tools for analysis of social networks
  5. apply social networks to achieve individual user or business goals
  6. design information and communication systems based on social networks

Forms of Teaching


The lectures take place for 2 hours per week.

Seminars and workshops

The seminars (covering publications related to social networks) are submitted in written form according to a predefined template. Additionally, students present their seminars and participate in discussions within a specified timeframe.

Independent assignments

The projects are done in teams.


The laboratory exercise is done individually. There is one exercise in each cycle.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 20 % 25 % 20 % 25 %
Class participation 0 % 15 % 0 % 15 %
Seminar/Project 37.5 % 40 % 37.5 % 40 %
Mid Term Exam: Written 0 % 5 % 0 %
Final Exam: Oral 5 %
Exam: Oral 10 %

Class participation: attendance at lectures + active engagement in all forms of instruction (including actively participating in discussions and asking questions during student presentations) + student seminar and presentation.

Week by Week Schedule

  1. Social networks: Introduction to the subject.
  2. Development of social networks
  3. Social networks today
  4. Challenges (and opportunities) facing social networking
  5. Social network-based programming
  6. Examples of research on popular social networks
  7. Security and privacy of social networks
  8. Midterm exam
  9. Theoretical aspects of social network analysis
  10. Practical applications of social network analysis
  11. Social media marketing
  12. The impact of social networks on everyday life
  13. Social network-based recommender systems 1
  14. Social network-based recommender systems 2
  15. Final exam


Matthew O. Jackson (2010.), Social and Economic Networks, Princeton University Press
Alain Barrat, Marc Barthélemy, Alessandro Vespignani (2008.), Dynamical Processes on Complex Networks, Cambridge University Press
Nahed Amin Azab (2012.), Cases on Web 2.0 in Developing Countries: Studies on Implementation, Application, and Use, IGI Global
Jennifer Golbeck (2013.), Analyzing the Social Web, Newnes
Ajith Abraham (2012.), Computational Social Networks, Springer Science & Business Media

For students


ID 223063
  Winter semester
L0 English Level
L1 e-Learning
30 Lectures
5 Seminar
0 Exercises
13 Laboratory exercises
0 Project laboratory
0 Physical education excercises

Grading System

85 Excellent
75 Very Good
65 Good
55 Sufficient