Network Analysis (Autumn 2020)
Official course description:
Course info
Programme
Staff
Course semester
Exam
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:
- Challenges & applications of network science: the many surprising properties of real-world networks.
- Linear algebra applications to network analysis.
- More graph properties & fitting power law degree distributions.
- Graph models #1
- Graph models #2
- Link prediction
- How to deal with noisy data: projection, backboning, and sampling
- The mesoscale: homophily, core-periphery, and hierarchies.
- Graph mining
- Community discovery #1: partitions & evaluations.
- Community discovery #2: overlaps & hierarchies.
- Spreading processes #1
- Spreading processes #2
- 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.
- Challenges & applications of network science: the many surprising properties of real-world networks.
- Linear algebra applications to network analysis.
- More graph properties & fitting power law degree distributions.
- Graph models #1
- Graph models #2
- Link prediction
- How to deal with noisy data: projection, backboning, and sampling
- The mesoscale: homophily, core-periphery, and hierarchies.
- Graph mining
- Community discovery #1: partitions & evaluations.
- Community discovery #2: overlaps & hierarchies.
- Spreading processes #1
- Spreading processes #2
- 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.
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
- 5-6
15 minutes
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.