Advanced Topics in Network Science (ANTS)
Course lecturers
Lovro Šubelj (R 2.49) & Jure Leskovec (Stanford)
Outline & objective
Networks or graphs are ubiquitous in everyday life. Examples include online social networks, the Web, wiring of a neural system, references between WikiLeaks cables, Supervizor, terrorist affiliations, plumbing systems and your brain. Many such real networks
reveal characteristic patterns of connectedness that are far from regular or random. Networks have thus been a prominent tool for investigating real-world systems since the 18th century. However, while small networks can be drawn by hand and analyzed
by a naked eye, real networks require specialized computer algorithms, techniques and models. This led to the emergence of a new scientific field about 20 years ago denoted network science.
The course will first introduce the language of networks and review fundamental concepts and techniques for the analysis of large real networks. In the main part of the course, the students will learn about selected advanced topics in network science
with special emphasis on the practical applicability of the presented approaches. The topics will include node metrics, clusters and patterns, large-scale network structure, network sampling, comparison, modeling, mining, inference, visualization
and dynamics.
The lectures will give a broad theoretical background that will suffice for a proper comprehension of the topics covered, whereas the main part of the lectures will be devoted to their practical applications found in the literature. The objective of the
course is not to give a comprehensive theoretical discussion or in-depth review on any of the topics, but to present a rich set of network science tools that students could use in their own PhD work. The latter will be the main part of the coursework.
Except for a clearly identified PhD topic, there are no specific prerequisites for the course. However, the students will benefit from a solid knowledge in graph theory, probability theory, statistics and linear algebra, good programming skills in a language
of their choice (e.g. C/C++, Python, Java), and familiarity with research work and scientific writing.
Literature & readings
The course notes, handouts, videos, readings and other materials will be posted periodically on this web page. The course literature will be given in English in the form of lecture handouts with references to relevant scientific papers. The students are
expected to search for additional literature on their own. Chapters from the following course books will be recommended as background reading.
- Barabási, A.-L., Network Science (Cambridge University Press, 2016).
- Newman, M.E.J., Networks: An Introduction (Oxford University Press, 2010).
- Easley, D. & Kleinberg, J., Networks, Crowds, and Markets (Cambridge University Press, 2010).
- de Nooy, W., Mrvar, A. & Batagelj, V., Exploratory Social Network Analysis (Cambridge University Press, 2011).
- Estrada, E. & Knight, P.A., A First Course in Network Theory (Oxford University Press, 2015).
Course assignments
Course assignments will be adjusted to individual students on a case-by-case basis.
