Course: Applied Graph Theory
Code: 3ФЕИТ08001
ECTS points: 6 ECTS
Number of classes per week: 3+0+0+3
Lecturer: Assoc. Prof. Dr. Vesna Andova
Course Goals (acquired competencies): After finishing this course, the students should 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. Erdos-Reny model for random graphs. 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. Biological networks. Software Pajek. Fullerenes and nanotubes as graph structures.
Literature:
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Required Literature |
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No. |
Author |
Title |
Publisher |
Year |
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1 |
A. Bondy, U.S.R. Murty | Graph Theory | Springer | 2008 |
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2 |
M. Newmann | Networks: An Introduction | Oxford University Press | 2010 |
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3 |
E.Estrada | The Structure of Complex Networks | Oxford University Press | 2012 |
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Additional Literature |
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No. |
Author |
Title |
Publisher |
Year |
|
1 |
R. Distel | Graph Theory | Springer-Verlag | 2010 |
|
2 |
U. Brandes, T. Erlebach | Network Analysis: Methodological Foundations | Springer | 2005 |
