Course: Complex Networks

Code: 3ФЕИТ08008

ECTS points: 6 ЕКТС

Number of classes per week: 3+0+0+3

Lecturer: Associate Professor Dr Vesna Andova

Course Goals (acquired competencies): After finishing this course, the students will deal with the basic concepts of classical graph theory and different models of random graphs.  They should learn and deal with different measures for large graphs (complex networks), and apply these measures in various real life networks.

Course Syllabus: Introduction to Graph Theory. Probability method: basic method, linearity of expectation, second moment. Different random graph models: Erdos-Reny model, Watz-Strogatz model, Barabasi model etc. Threshold function. Flow. Complex networks: small world, scale free networks, selfsimilar networks.   Centrality measures, vulnerability of networks, degree distribution and correlation, clustering coefficient, and other measures.   Dynamic of networks. Analysis od different real networks such as biological networks, transporting networks, fullerenes and nanotubes as graph structures etc.

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