250116 VO Introduction to complex network analysis (2023W)

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