Network Science

Introduction and Objectives

Network Science is an interdisciplinary field of research that has become synonym with the study of multiple complex systems that pervade social and economic systems. Network refer to representations of systems whose constituents are linked together because of social ties, information flow, economic relations, etc. Network modelling is a methodology with ample applications in modern data-intensive fields which has multiple applications in management, marketing, informatics, among multiple others.  

The course covers a wide range of topics: it starts with an introduction to the basic concepts of networks; it then deals with the most important properties that real-world networks exhibit, and how they can be modelled; then, it introduces network analytic techniques to uncover the most important properties of empirical networks. Finally, an introduction to the diffusion of technologies, opinions, and rumours (and viruses!) is taught. During the course, special emphasis is employed in introducing network analysis and visualisation tools.  

The course is highly interactive. All the lectures consist of a theoretical part, then, the students must solve (in small groups and always supported by the instructors) some practical exercises themselves. This permits them to gain direct experience and familiarity with the concepts taught and the techniques involved. In this participatory environment, multiple exercises and the creation of visualizations play an important role. 

Instructors:

Prof. Dr. Claudio J. Tessone (theory and practice)

Dr. Nicolo’ Vallarano(theory and practice)

Dr. Taehoon Kim(theory and practice)

Yu Gao (practice)

Yu Zhang (practice)

Benjamin Kraner(practice)

Type:
Lecture

Target Audience:
This course is acknowledged for MA students and is assigned to the Core elective areas „Wahlpflichtbereich”: 

Frequency:
Each fall semester

ECTS: 6

Content:
A detailed introduction to the most paradigmatic network science methods and network generation models with a focus on understanding the aims and applications of this branch of science. 

Language:
English

Prerequisites:
Fundamental courses in statistics (e.g. Empirical Methods, Programming). Solid programming skills (or the willingness to develop this knowledge prior to the lecture) are a necessary requirement. The programming language in which the exercises are to be solved is not relevant. We can support you if the code is written in Python, etc. Python will be used during the exercises as example.

Grading:
Assignments given in class + Final Project.

Dates and Location:
Mondays 08.00 -12.00
16th September - 16th December 2024
Location: TBD

Registration:
Don’t forget to officially register yourself using the registration tools at the University of Zurich.

Syllabus:

The syllabus will be published soon.

OLAT element:

TBD