Official course description, subject to change:
Basic info last published 2/04-20

Network Analysis

Course info
Language:
English
ECTS points:
7.5
Course code:
BSNEANA1KU
Participants max:
75
Offered to guest students:
yes
Offered to exchange students:
Offered as a single subject:
yes
Price (single subject):
10625 DKK (incl. vat)
Programme
Level:
Bachelor
Programme:
BSc in Data Science
Staff
Course manager
Assistant Professor
Teacher
Associate Professor
Course semester
Semester
Efterår 2020
Start
24 August 2020
End
22 January 2021
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract
The course is focused on forming the basis of a network scientist. The final objective is to have the students being able to fully appreciate the difficulties of the problem of finding communities in social networks. To achieve this objective, a complete knowledge of network science is required. Each concept necessary to understand communities in networks has to be fleshed out in previous lectures, and the concepts on which it depends have to be presented beforehand.
Description

Networks science is a thriving field of study. The reason of its popularity is its ability to represent very complex phenomena with the very simple model of a graph. With network science one can analyse global societal patterns, catastrophic breakdowns of distributed infrastructures, the metabolic pathways in humans at the basis of diseases, among many examples.

  1. Challenges & applications of network science: the many surprising properties of real-world networks.
  2. Linear algebra applications to network analysis.
  3. More graph properties & fitting power law degree distributions.
  4. Graph models.
  5. Spreading processes.
  6. Link prediction.
  7. Exam simulation #1.
  8. How to deal with noisy data: projection, backboning, and sampling.
  9. The mesoscale: homophily, core-periphery, and hierarchies.
  10. Graph mining.
  11. Community discovery #1: partitions & evaluations.
  12. Community discovery #2: overlaps & hierarchies.
  13. Community discovery #3: bipartite & multilayer.
  14. Exam simulation #2.
Formal prerequisites

The students should have a basic understanding of statistics 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 and summarize their main characteristics and how those affect network-based processes (e.g. propagation)
  • Identify community structures in network and provide an appropriate interpretation of these structures.
Ordinary exam
Exam type:
D: Submission of written work with following oral, External (7-point scale)
Exam variation: