Czech version
Network Science I (MASI)
Fall 2024
Course description:
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.
Grading (Attendance at lectures and seminars is compulsory, as well as preparation for the seminars):
- Ongoing activities: 17 - 33 points
- Written exam: 17 - 33 points
- Data analysis: 17 - 34 points,
Lectures and Seminars (labs) - published in LMS Moodle
Literature and sources:
-
Literature
- Albert-Laszlo Barabasi.
Network Science
- Zaki, M. J., Meira Jr, W. Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press. 2014.
- Douglas A. Luke. A User’s Guide to Network Analysis in R. Springer. 2015. ISSN 2197-5744.
- Mark Newman. Networks: An Introduction. Oxford University Press, 2010. ISBN 978-0199206650.
- Zinoviev D. Complex Network Analysis in Python. The Pragmatic Programmers, LLC. 2018. ISBN-13: 978-1-68050-269-5.
-
Tools for network analysis and visualization
- Pajek - Program for Large Network Analysis,
Pajek
- NodeXL - Tempalte for Excel,
NodeXL
- SNAP - Stanford Network Analysis Project,
SNAP
-
Gephi
, Graphviz etc.
-
Visual Complexity
- D3.js - JavaScript library for manipulating documents based on data,
D3.js
Course Outline:
1. Introduction to network data analysis. Basic concepts, representations of network data.
2. Statistics for network analysis.
3. Basic global and local properties (centralities, path-based properties)
4. Basic global and local properties (structural properties)
5. Network robustness
6. Basic models - random graph, small world, preferential attachment
7. Methods of network construction from vector data.
8. Communities and network community structure
9. Network models generating community structure
10. Correlation in networks
11. Sampling methods for network data
12. Network visualization