Official course description:

Full info last published 15/05-23
Course info
Language:
English
ECTS points:
7.5
Course code:
KSADNES1KU
Participants max:
37
Offered to guest students:
yes
Offered to exchange students:
yes
Offered as a single subject:
yes
Price for EU/EEA citizens (Single Subject):
10625 DKK
Programme
Level:
MSc. Master
Programme:
MSc in Data Science
Staff
Course manager
Michele Coscia
Associate Professor
Course semester
Semester
Efterår 2023
Start
28 August 2023
End
26 January 2024
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 an advanced understanding of the current state of the art of network science. The final objective is to have the students master computational techniques to solve advanced network problems, to be able to contribute to the development of network analysis, and to appreciate the limitations and future developments of scientific papers dealing with network problems in real-world data.

 

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 node role classification; 
  2. High-order dynamics – interactions between nodes that go beyond their connections; 
  3. Deep learning on graphs; 
  4. Graph summarization; 
  5. Frequent subgraph mining; 
  6. Network distances. 

During the course, you will be reading classic articles from the network science literature selected by the teacher. You will choose one as the main topic for your oral examination. 

Formal prerequisites

The student should have an advanced understanding of statistics, linear algebra, machine learning, and Python programming.  

The student must have taken the Network Analysis course offered in the BSc Data Science program, or an equivalent course covering the same topics.

If the student did not attend a network analysis course at ITU or elsewhere, they should request access to the LearnIT page of the Network Analysis course and study the materials. Specifically, they should focus on Chapters 3-11, 13-20, 23-27, and 31-34 of the textbook (https://www.networkatlas.eu/).

Intended learning outcomes

After the course, the student should be able to:

  • Identify the relevant literature to follow the methodology of a network science research paper
  • Reproduce the analysis of a network science research paper
  • Select the most appropriate computational tool to address a specific question on a network
  • Analyse real world social networks by applying advanced network analysis tools like (e.g. graph neural networks)
  • Plan potential ways in which to customize an available tool to fit a new network feature or question
Learning activities

During the course, the instructors will provide readings instruction one week before each lecture: the students are supposed to read the material before class. Readings include chapters from the textbook and research papers. 

Each exercise session will involve dissecting the methodology behind a specific network analysis tool. Students are encouraged to hand-in their work and get feedback from teachers and TAs. 

Course literature

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

Additional material: pre-selected research papers, from the teacher (paper list still TBD), 

Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 30%
  • Lectures: 25%
  • Exercises: 30%
  • Exam with preparation: 15%
Ordinary exam
Exam type:
B: Oral exam, External (7-point scale)
Exam variation:
B1GH: Oral exam in group, with time for preparation. Home.
Exam duration for the preparation:
Small groups (two or three students) will choose one paper among the ones pre-selected by the teacher. They will have to communicate their choice at the end of the course.
The oral examination consists in: (i) a brief overview of the paper from the students (not a formal presentation, ~2-4 minutes); (ii) a series of questions about the specific paper chosen by the students, taken as a group; (iii) individual questions on the topics included in the examination curriculum (regardless of whether they are part of the chosen paper or not).
Exam duration per student for the oral exam:
20 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.


reexam
Exam type:
B: Oral exam, External (7-point scale)
Exam variation:
B1H: Oral exam with time for preparation. Home.
Exam duration per student for the oral exam:
20 minutes

Time and date
Ordinary Exam Thu, 18 Jan 2024, 09:00 - 21:00
Ordinary Exam Fri, 19 Jan 2024, 09:00 - 21:00
Reexam Tue, 5 Mar 2024, 12:00 - 15:00