Bachelor
2024/2025
Analysis and Visualization of Networks
Type:
Elective course (Applied Mathematics and Information Science)
Area of studies:
Applied Mathematics and Information Science
Delivered by:
School of Data Analysis and Artificial Intelligence
Where:
Faculty of Computer Science
When:
4 year, 3 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Ilia Karpov
Language:
English
ECTS credits:
4
Course Syllabus
Abstract
This course introduces methods and algorithms for analysing and visualizing graphs and networks. The course includes a review of modern network analysis and visualization techniques with their applications in various domains. We will concern on three main topics: network analysis methods based on applied graph theory, graph drawing algorithms, applications of network analysis and visualization to real problems.
Learning Objectives
- To know the classification of main network analysis tasks, basic methods and algorithms, most popular software tools.
- To be able to define a graph-theoretic description of network analysis task and corresponding network visualization requirements.
- To be able to select reasonably an appropriate project solutions and tools for network analysis workflow.
- To be able to develop a new variants of graph drawing algorithms.
Expected Learning Outcomes
- Students design and solve graph-theoretical mathematical models.
- Students know the basic concepts of analysing and visualizing graphs and networks.
- Students select and justify appropriate graph drawing method and algorithm.
- Students use development techniques, skills and tools necessary to network visualization.
Course Contents
- Introduction
- Graphs, topology and geometry
- Visualization of small graphs: drawing and layout
- Visualization of large graphs
- Interactive visualization of graphs
- Visualization of graphs and networks in real world applications
- Modern trends in graph databases and network analysis software
Bibliography
Recommended Core Bibliography
- Brath, R., Jonker, D. Graph Analysis and Visualization: Discovering Business Opportunity in Linked Data. – Wiley, 2015. – 513 pp.
Recommended Additional Bibliography
- Newman, M., Watts, D. J., and Barabási, A. The Structure and Dynamics of Networks. – Princeton University Press, 2006. – 592 pp.