Network Analysis (Autumn 2024)

Abstract

Networks are all around us: We are ourselves, as individuals, the units of a network of social relationships of different kinds; the Internet and the highway system can be modelled as networks embedded in space; networks can be also entities defined in an abstract space, such as networks of acquaintances or collaborations  between individuals. This course aims at providing the computational tools to study these networks and form the basis of network scientists. The final objective is to have the students to solve practical network problems, to be able to perform a network analysis, and to fully appreciate the difficulties of a network problem in real-world data. The course will have a special focus on social networks.

Description

Network science is a thriving field of study. The reason of its popularity is its ability to represent very complex phenomena with very simple models (graphs). With network science one can analyze: global societal patterns, catastrophic breakdowns of distributed infrastructures, the metabolic pathways in humans at the basis of diseases, among many examples. The topics covered during the course are:

1. Basic & advanced graph representations of network data.
2. Linear algebra applications to network analysis.
3. Degree, distributions, & fitting power laws.
4. Network models.
5. Network data cleaning: projection, backboning, sampling.
6. Random walks.
7. Spreading processes.
8. Link prediction.
9. The mesoscale: homophily, core-periphery, hierarchies.
10. Community discovery

During the course, you will be part of a group with the objective of answering a network question. The teachers and TAs will supervise you, and the project will be the basis of your oral examination.

Formal prerequisites

The students should have a basic understanding of statistics, linear algebra, and Python programming.

The course is required for BSc students of Data Science third semester. 
The student must always meet the admission requirements of the IT University.

Intended learning outcomes

After the course, the student should be able to:

• Define various types of network structure and calculate the main descriptive metrics
• Describe the main characteristics of a given network structure
• Analyse real world social networks, summarize their main characteristics, and reflect on how these characteristics affect network-based processes (e.g. propagation)
• Identify community structures in network and provide an appropriate interpretation of these structures.
• Analyze and compare different network models

Ordinary exam

Exam type:
D: Submission of written work with following oral, External (7-point scale)
Exam variation:
D1G: Submission for groups with following oral exam based on the submission. Shared responsibility for the report.