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Course module: INFOMNWSC
INFOMNWSC
Network science
Course infoSchedule
Course codeINFOMNWSC
ECTS Credits7.5
Category / LevelM (M (Master))
Course typeCourse
Language of instructionEnglish
Offered byFaculty of Science; Graduate School of Natural Sciences; Graduate School of Natural Sciences;
Contact persondr. E.J. van Leeuwen
E-mailE.J.vanLeeuwen@uu.nl
Lecturers
Course contact
dr. E.J. van Leeuwen
Other courses by this lecturer
Lecturer
dr. E.J. van Leeuwen
Other courses by this lecturer
Lecturer
dr. J.M.M. van Rooij
Other courses by this lecturer
Teaching period
4  (25/04/2022 to 08/07/2022)
Teaching period in which the course begins
4
Time slotA: A (MON-morning, TUE-afternoon, WED-morn)
Study mode
Full-time
Enrolment periodfrom 31/01/2022 up to and including 27/02/2022
Course application processOsiris Student
Enrolling through OSIRISYes
Enrolment open to students taking subsidiary coursesYes
Pre-enrolmentNo
Post-registrationYes
Post-registration openfrom 04/04/2022 up to and including 05/04/2022
Waiting listYes
Course placement processadministratie onderwijsinstituut
Course goals
After completing this course, the student will have:
  • knowledge of important (random) graph models and properties for networks
  • knowledge of important dynamics of networks
  • knowledge of important processes on networks
  • knowledge of important algorithmic challenges and solutions for the analysis of very large networks
  • the ability to analyze properties of random graphs Ability to analyze properties of dynamics and processes on networks
  • the ability to survey literature of an advanced topic in network algorithms
  • the ability to experimentally study an advanced topic in network algorithms
  • the ability to hold a brief presentation of an advanced topic in network algorithms.
  • the ability to provide/use feedback to/from peers.
Assessment
Mini-tests (25% of the final mark), term paper (55%), flash talk (10%), peer reviews (10%).

Prerequisites
The course assumes that you have basic skills in algorithms and mathematics: familiarity with basic graph algorithms (shortest paths, flows), such as offered in INFOAL Algoritmiek, and basic understanding of NP-completeness, such as offered in INFOAL or INFOMADS Algorithms for Decision Support. Having taken INFOAN Algorithms and Networks is very helpful, but not required. During the class, we also work with basic probabilities and some integrals.
 
Content
Network science is an exciting new field that studies large and complex networks, such as social, biological, and computer networks.
The class will address topics from network structure and growth to the spread of epidemics. We study the diverse algorithmic techniques and mathematical models that are used to analyze such large networks, and give an in-depth description of the theoretical results that underlie them.
Some topics are random graphs, giant components, power laws, percolation, spreading phenomena, community detection, basic algorithms for network science, lower bounds and advanced algorithms for polynomial-time problems, sampling algorithms, streaming algorithms, sublinear algorithms, and graph partitioning algorithms.

Course form
Lectures, tutorials, term paper, peer feedback.

A major component of the class is for students to perform their own experimental study on algorithms for the community detection problem.
The results of this study will be presented in the term paper. This term paper will be reviewed by your student peers and you will present it in a joint seminar at the end of the course.

Literature
Recommended:
  • A. Barabasi, "Network Science", for free online
  • M.E.J. Newman, "Networks", 2nd edition (2018).
The class is mostly based on the Barabasi book, with some parts taken from Newman. Using either book is sufficient for the class.
Competencies
-
Entry requirements
You must meet the following requirements
  • Assigned study entrance permit for the master
Required materials
-
Instructional formats
Seminar

Tests
Final result
Test weight100
Minimum grade-

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