Advanced Topics in Network Science
This course will first introduce the language of networks and review the fundamental concepts and techniques for the analysis of large real-world networks. In the main part of the course, the students will learn about selected advanced topics in network science with special emphasis on the practical applicability of the presented approaches. The topics will include node metrics, groups and patterns, large-scale network structure, network sampling, comparison, modeling, mining, inference, visualization and dynamics. The last part of the course will be devoted to invited talks on network science from the perspective of mathematicians, physicists, social scientists and other.
The objective of the course is not to give a comprehensive theoretical discussion or in-depth review on any of the topics, but to present a rich set of network science tools that students could use in their own doctoral research work. The latter will be the main part of the coursework.
Except for a clearly identified doctoral topic, there are no specific prerequisites for the course. However, the students will benefit from a solid knowledge in graph theory, probability theory and linear algebra, good programming skills in a language of their choice, and familiarity with research work and scientific writing.
- nosilec: Lovro Šubelj