460-4141 – Network Science I (MAS I)

The course aims to introduce complex networks focusing on their types (social, communication, biological, etc.), properties, models, and methods of their analysis. After completing the course, the student will understand the principles that affect the properties of networks. will be able to apply methods related to the analysis of these properties and implement prototypes of selected methods and models. Furthermore, he will be able to use tools and libraries for analysis and visualization of networks, and after the application of network analysis methods will be able to assess the relevance of the results and find an understandable interpretation.

Lectures are focused on the theoretical background of properties, models, and analytical methods so that students are able to decide what purpose the particular methods are suitable for, how to set and apply them, what outcomes can be obtained through their application and how these outcomes can be interpreted. Seminars are focused on experiments with suitable data sets, implementations of method prototypes, experimenting with tools and libraries for analysis and visualization of network data, and evaluating the experiments' results.

[1] Barabási, L-A. (2016). Network science. Cambridge University Press, 2016.

[1] Zaki, M. J., Meira Jr, W. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press. [2] Newman, M. (2010). Networks: An Introduction. Oxford University Press. [3] Leskovec, J., Rajaraman, A., Ullman, J. D. (2014). Mining of massive datasets. Cambridge University Press.