Official course description:
Full info last published 17/05-21

Network Analysis

Course info
Language:
English
ECTS points:
7.5
Course code:
BSNEANA1KU
Participants max:
97
Offered to guest students:
yes
Offered to exchange students:
yes
Offered as a single subject:
yes
Price (single subject):
10625 DKK (incl. vat)
Programme
Level:
Bachelor
Programme:
BSc in Data Science
Staff
Course manager
Associate Professor
Teacher
Associate Professor
Teaching Assistant
Teaching Assistant (TA)
Teaching Assistant
Teaching Assistant (TA)
Teaching Assistant
Teaching Assistant (TA)
Course semester
Semester
Efterår 2021
Start
30 August 2021
End
31 December 2021
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7-trinsskala
Exam Language
GB
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:

Learning activities

14 weeks of teaching consisting of lectures and exercises

During the course, the instructors will provide readings instruction one week before each lecture: the students are supposed to read the material before class. Each exercise session will consist of several questions: students are encouraged to hand-in their answers and get feedback from teachers and TAs.

  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 #1: partitions & evaluation.
  11. Community discovery #2: overlap & hierarchy.
  12. Group supervision for projects
  13. Group supervision for projects
  14. Group supervision for projects

Course literature

"The Atlas for the Aspiring Network Scientist" by M Coscia (course manager), available for free at: https://www.networkatlas.eu/

Additional material (teacher's notes) provided via LearnIT.

Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 20%
  • Lectures: 20%
  • Exercises: 20%
  • Project work, supervision included: 30%
  • Exam with preparation: 10%
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.
Exam submisson description:
The group project will consist of a network analysis of a real-world data set. A checklist for the network analysis will be provided during the course.
Group submission:
Group
  • Group size 5-6
Exam duration per student for the oral exam:
15 minutes
Group exam form:
Mixed exam 1 : Individual and joint student presentation followed by an individual and a group dialogue. The students make a joint presentation followed by a group dialogue. Subsequently the students are having individual examination with presentation and / or dialogue with the supervisor and external examiner while the rest of the group is outside the room.

Time and date
Ordinary Exam - submission Wed, 22 Dec 2021, 08:00 - 14:00
Ordinary Exam Mon, 10 Jan 2022, 09:00 - 21:00
Ordinary Exam Tue, 11 Jan 2022, 09:00 - 21:00
Ordinary Exam Wed, 12 Jan 2022, 09:00 - 21:00
Ordinary Exam Thu, 13 Jan 2022, 09:00 - 21:00
Ordinary Exam Fri, 14 Jan 2022, 09:00 - 21:00