Official course description:
Full info last published 11/08-20

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
Teaching Assistant
Teaching Assistant (TA)
Teaching Assistant
Teaching Assistant (TA)
Course semester
Semester
Efterår 2020
Start
24 August 2020
End
31 January 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

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. The topics covered during the course are:

  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 #1
  5. Graph models #2
  6. Link prediction
  7. How to deal with noisy data: projection, backboning, and sampling
  8. The mesoscale: homophily, core-periphery, and hierarchies.
  9. Graph mining
  10. Community discovery #1: partitions & evaluations.
  11. Community discovery #2: overlaps & hierarchies.
  12. Spreading processes #1
  13. Spreading processes #2
  14. Open class

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
Learning activities

14 weeks of teaching consisting of lectures and exercises

During the course, the instructors will give two assignments. Each assignment contains a number of exercises about the measures, models, and algorithms covered in class. Students are encouraged to hand-in their assignments and get feedback from teachers and TAs.

  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 #1
  5. Graph models #2
  6. Link prediction
  7. How to deal with noisy data: projection, backboning, and sampling
  8. The mesoscale: homophily, core-periphery, and hierarchies.
  9. Graph mining
  10. Community discovery #1: partitions & evaluations.
  11. Community discovery #2: overlaps & hierarchies.
  12. Spreading processes #1
  13. Spreading processes #2
  14. Open class

Course literature

Network Science” by A-L. Barabasi (free book available at http://networksciencebook.com/). 

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: 10%
  • Lectures: 20%
  • Exercises: 20%
  • Assignments: 10%
  • 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
  • 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 Mon, 4 Jan 2021, 08:00 - 14:00