Universität Wien
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250116 VO Introduction to complex network analysis (2023W)

3.00 ECTS (2.00 SWS), SPL 25 - Mathematik
VOR-ORT
Moodle

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Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Prüfungstermine

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

The first two dates on October 3rd and 10th are cancelled. The first lecture will take place on October 17th.

  • Dienstag 03.10. 08:00 - 09:30 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 10.10. 08:00 - 09:30 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 17.10. 08:00 - 09:30 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 24.10. 08:00 - 09:30 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 31.10. 08:00 - 09:30 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 07.11. 08:00 - 09:30 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 14.11. 08:00 - 09:30 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 21.11. 08:00 - 09:30 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 28.11. 08:00 - 09:30 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 05.12. 08:00 - 09:30 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 12.12. 08:00 - 09:30 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 09.01. 08:00 - 09:30 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 16.01. 08:00 - 09:30 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 23.01. 08:00 - 09:30 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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 250117
https://ufind.univie.ac.at/de/course.html?lv=250117&semester=2023W

Art der Leistungskontrolle und erlaubte Hilfsmittel

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

Mindestanforderungen und Beurteilungsmaßstab

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

Prüfungsstoff

Entire content of the lecture.

Literatur

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.

Zuordnung im Vorlesungsverzeichnis

MBIV; MAMV

Letzte Änderung: Mo 15.01.2024 13:26