Intro

This site contains the description of the course Complex network analysis (NDMI096) organized primarily for master students in Czech as well as English versions. This course loosely follows on from the subject Graphs and Networks (NDMI110), but the connection is relatively weak and most topics are taught from scratch.

Description

Many real-world systems possess a natural network structure. Examples include social systems such as Facebook, Twitter, or real-world communities, biological systems such as the human brain, protein interaction systems, or metabolic networks, technological systems such as the Internet, or World-Wide-Web, financial systems such as stock markets, or trade networks, and many others. We can often provide much information about these systems using analysis of the corresponding networks, such as characterizing Alzheimer's disease effects, financial crisis structure, construction of autonomous recommender systems, finding bottlenecks of computer networks, or predicting the effects of the newly constructed drugs. This course aims to introduce such a vast area of complex network theory concentrated on graph-theoretic properties of these networks and corresponding algorithmic solutions.

This course focuses on advanced topics in complex networks with a strong emphasis on their structural properties. Key themes include random graph theory with emphasis to real-world type networks, advanced community characterization (beyond basic detection, including robustness and resolution issues), network reconstructibility and connected topics from the distribution of network characteristics (e.g., centralities, clustering, motifs, path-based measures). We also cover spectral properties of networks — including spectral characteristics beyond PageRank.

Organization

The course is organized in lectures providing basic theory on topics given above. Seminars are organized The seminars are organized as readings of recent results.

Continuous evaluation is based on presentation of topics from the complex network theory by students.

References

This section contains the list of references basic and advanced for the course, some of which are available online..

Basic references
Albert-Laszco Barabasi	MEJ Newmann	V. Latora, V. Nicosia, G. Russo
Network Science	Networks: an introduction	Complex networks
Cambridge University Press, 2016	Oxford University Press, 2018	Cambridge University Press, 2017
Extended references
D. Easly, J. Kleinberg	A. Barrat, M. Barthelemy, A. Vespignani
Networks, Crowds and Markets	Dynamical Processes on Complex Networks
Cambridge University Press, 2010	Cambridge University Press, 2012

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