### Description

Data Science Master's Programme is responsible for the course.

The course belongs to Specialization Studies > Algorithmic Data Science.

Elective course with permanent offering.

The course is available to students from other degree programmes.

**Prerequisites in terms of knowledge**

Basic data structures and algorithms. Some experience in a modern programming language

**Prerequisites for students in the Data Science programme, in terms of courses**

None

**Prerequisites for other students in terms of courses**

None

**Recommended preceding courses**

CSM12101 Design and Analysis of Algorithms

CSM12101 Design and Analysis of Algorithms

Students will learn to:

- use algorithms to measure basic quantities (e.g., centrality measures) associated with networks;
- describe network phenomena (e.g., the spread of epidemic diseases and the propagation of information in social networks) in terms of basic network models;
- use algorithms to predict the effect of network phenomena.

Recommended for 1st year of Master's studies.

The course will be offered in the spring term (period 3), every year.

The course will cover the following topics: basics of graph theory; network formation mechanisms; information cascades and epidemics; population models, power laws, and rich-get-richer phenomena; the small-world phenomenon.

The course follows the book "Networks, Crowds, and Markets: Reasoning About a Highly Connected World" by David Easley and Jon Kleinberg, with emphasis on Parts I, IV, V and VI.

A full-draft copy of the book can be obtained from the website of the book at https://www.cs.cornell.edu/home/kleinber/networks-book/.

The course will be centered around lectures delivered by the instructors.

The students will complete three homework assignments that may involve programming tasks.

All course material will be available online for students of the course, but it is strongly recommended that students attend the lectures.

No strict attendance requirements.

Students must complete a minimum of grades across all deliverables (homework assignments) and exam of the course.

Michael Mathioudakis, Pan Hui