Network Science

Abstract: 

Online social media, organization networks, and decentralized computing systems are just a few examples of how interlinked our world has become. The goal of the course is to provide MA students with an introduction to network theory and social network analysis. This course is grounded in theory and applied towards practice. Students will get an overview of network science, starting from basic network models to advanced topics such as temporal networks and percolation. Besides, it also introduces students to the tools that are typically used in network science. Students should be able to develop abilities to identify and apply suitable methods to deal with network analysis problems.

Instructors:

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

Dr. Nicolo’ Vallarano(theory and practice)

Dr. Jian-hong Lin(theory and practice)

Yu Gao (practice)

Yu Zhang (practice)

Benjamin Kraner (practice)

Type:
Lecture

Target Audience:
Master students assigned to “Wahlpflichtbereich" INF1, INF4, INF5, BWL 4, OEC

Frequency:
Each fall semester

ECTS: 6

Content:
Advanced network science including both network theory and social network analysis.

Language:
English

Prerequisites:
Fundamental courses in statistics (e.g. Empirical Method MOEC0021). Solid R and/or Python programming skills (or the willingness to develop the knowledge PRIOR to the lecture) are a necessary requirement.

Grading:
Assignments given in class + Final Exam.

Dates and Location:
Mondays 08.00 -12.00
18th September - 18th December 2023
Location: BIN-0-K.02

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

Syllabus:

The syllabus is available for download here (PDF, 346 KB)

OLAT element:

https://lms.uzh.ch/auth/RepositoryEntry/17430414277/CourseNode/85421310414617