Complex Networks

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.

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




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

Study Programmes


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

For students


ID 222622
  Winter semester
L3 English Level
L1 e-Learning
30 Lectures
9 Laboratory exercises

Grading System

Very Good