Course Description

The course introduces students to the rapidly developing interdisciplinary field of study of structural data and patterns in them. As part of the course, we will consider methods for statistical and structural analysis of networks, models for the formation and evolution of networks and processes, machine learning on graphs. Particular attention will be paid to practical analysis and visualization of real networks using available software tools, deep learning on graphs, modern social network analysis libraries.

As a result, students should: Know: - the basic principles behind the the existing social network models and concepts - advantages of existing social network analysis packages

Be able to: - get necessary data for research and applied projects - perform basic social network description - use existing tools for network modelling - criticize constructively and determine existing issues with applied SNA tasks

Have: - an understanding of the basic principles of contemporary social network analyses - the skill to meaningfully develop an appropriate graph models - the skill to visualize graph data

Basic knowledge of python programming language and statistics are required for this course.