Universität Wien

250074 VO Introduction to complex network analysis (2025W)

3.00 ECTS (2.00 SWS), SPL 25 - Mathematik
Moodle
We 12.11. 16:45-18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
Register/Deregister for this course

Details

Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 01.10. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 08.10. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 15.10. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 22.10. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 29.10. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 05.11. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 19.11. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 26.11. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 03.12. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 10.12. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 17.12. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 07.01. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 14.01. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 21.01. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 28.01. 16:45 - 18:15 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock

Information

Aims, contents and method of the course

Networks provide a unifying framework for studying complex systems that consist of many interacting components. A wide range of systems can be understood as networks, from the molecular interactions within our cells, to computers connected across the globe via the internet, and social relationships between people. Network science is an interdisciplinary field drawing from graph theory, statistical physics, data science, but also social science and biology.
In this course we will introduce the fundamental concepts of network science and showcase key applications, with a particular focus on biological systems. Topics include the characterization of local and global network structures, random graph models and community detection. The course covers analytical models, as well as their computational implementation and application to real-world datasets.
The overarching goal of the course is to understand how studying network structures can help us understand the complex real-world systems that they represent. By the end of the course, students are expected to be able to identify, construct, and analyze networks by choosing and applying appropriate methods and algorithms. Students are also expected to be able to explain, both mathematically and conceptually, the key network concepts, models, and statistical properties, as well as their implications.
The course requires a good foundation in mathematics and statistics, basic knowledge in python programming is a plus, but not a prerequisite.
The exercises of this course will be discussed in the accompanying proseminar 250084
https://ufind.univie.ac.at/de/course.html?lv=250084&semester=2025W

Assessment and permitted materials

There will be a written exam at the end of the semester.

Minimum requirements and assessment criteria

At least 40% of all points in the final written exam.

Examination topics

Entire content of the lecture.

Reading list

The lecture will be based on the book Network Science by Albert-László Barabási (Cambridge University Press, 2016). The book can also be accessed online http://networksciencebook.com
Other literature:
M.E.J. Newman; The Structure and Function of Complex Networks; SIAM Rev., 45(2), 167–256 (2003) https://doi.org/10.1137/S003614450342480
R. Albert and A.-L. Barabási; Statistical mechanics of complex networks; Rev. Mod. Phys. 74, 47 (2002) https://doi.org/10.1103/RevModPhys.74.47
M.E.J. Newman; Networks; Oxford University Press (2018)
Python programming:
https://www.python.org/about/gettingstarted Official Python website with lots of resources for both beginners and experienced programmers.
https://www.learnpython.org A web-based interactive tutorial for beginners.
https://networkx.org A Python module for network analysis and visualization.
https://github.com/CambridgeUniversityPress/FirstCourseNetworkScience Python-based network science introduction.

Association in the course directory

MBIV; MAMV; ML2; MEL

Last modified: Tu 21.10.2025 13:47