Complex Networks

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

Lecturers

Prof.
Mile Šikić
Prof.
Vedran Podobnik

Course Description

The course is an introduction to the science of complex networks and their applications. Topics to be covered include the graph theory, data analysis, and applications to biology, sociology, technology, and other fields. Students will learn about the ongoing research in the field, and ultimately apply their knowledge to conduct their own analysis of a real network data set of their choosing as part of the final project.

Study Programmes

University graduate
[FER3-HR] Audio Technologies and Electroacoustics - profile
Elective Courses (1. semester) (3. semester)
[FER3-HR] Communication and Space Technologies - profile
Elective Courses (3. semester)
[FER3-HR] Computational Modelling in Engineering - profile
Elective Courses (3. semester)
[FER3-HR] Computer Engineering - profile
Elective Courses (3. semester)
Elective Courses of the Profile (3. semester)
[FER3-HR] Computer Science - profile
Elective Courses (3. semester)
[FER3-HR] Control Systems and Robotics - profile
Elective Courses (3. semester)
[FER3-HR] Data Science - profile
Elective Courses (3. semester)
Elective Courses of the Profile (3. semester)
[FER3-HR] Electrical Power Engineering - profile
Elective Courses (3. semester)
[FER3-HR] Electric Machines, Drives and Automation - profile
Elective Courses (3. semester)
[FER3-HR] Electronic and Computer Engineering - profile
Elective Courses (3. semester)
[FER3-HR] Electronics - profile
Elective Courses (3. semester)
[FER3-HR] Information and Communication Engineering - profile
Elective Courses (3. semester)
Elective Coursesof the Profile (3. semester)
[FER3-HR] Network Science - profile
Core-elective courses (3. semester)
[FER3-HR] Software Engineering and Information Systems - profile
Elective Courses (3. semester)

Learning Outcomes

  1. Explain basic concepts of complex networks
  2. Apply the knowledge gained to real networks
  3. Analyze data gathered from social networks

Forms of Teaching

Lectures

Lectures

Exercises

Coding tutorials

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Attendance 10 % 20 % 10 % 20 %
Final Exam: Written 0 % 80 %
Exam: Written 0 % 80 %

Week by Week Schedule

  1. Definition, Terms
  2. Erdos-Renyi random graphs, tree structure, giant component, Small-world (Watts-Strogatz) model
  3. Degree distributions
  4. Clustering
  5. Algorithms for computing degree distributions and clustering coefficients
  6. Network growth, preferential attachment, Barabasi-Albert model, power-law networks
  7. Centrality
  8. Not held
  9. Extremal paths and breadth-first search, maximum flows and minimum cuts, spanning trees
  10. Graph partitioning, community detection
  11. Spectral properties of adjacency matrix
  12. Structure of social network graphs
  13. Social network analysis
  14. Social network analysis
  15. Not held

Literature

(.), Network Science, Albert-László Barabási,
(.), Networks – an Introduction, Mark Newman, Oxford University Press,

For students

General

ID 252364
  Winter semester
5 ECTS
L0 English Level
L1 e-Learning
30 Lectures
0 Seminar
12 Exercises
0 Laboratory exercises
0 Project laboratory

Grading System

86 Excellent
74 Very Good
62 Good
50 Sufficient

Similar Courses