Department of Information and Computing Sciences

Departement Informatica Onderwijs
Bachelor Informatica Informatiekunde Kunstmatige intelligentie Master Computing Science Game&Media Technology Artifical Intelligence Human Computer Interaction Business Informatics

Onderwijs Informatica en Informatiekunde

Vak-informatie Informatica en Informatiekunde

Network science

Website:website containing additional information
Course code:INFOMNWSC
Credits:7.5 ECTS
Period:period 4 (week 17 through 26, i.e., 26-4-2021 through 2-7-2021; retake week 28)
Participants:up till now 0 subscriptions
Schedule:Official schedule representation can be found in MyTimetable
lecture          Erik Jan van Leeuwen
Johan van Rooij
Note:No up-to-date course description available.
Text below is from year 2019/2020
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.

List of topics
Random graphs, giant components, percolation, spreading phenomena, basic algorithms for network science, lower bounds for polynomial-time problems, sampling algorithms, streaming algorithms, sublinear algorithms, power laws, spreading phenomena, community detection, graph partitioning algorithms.

The course assumes that you have basic skills in algorithms and mathematics (some integrals and probabilities might be involved). The course assumes familiarity with basic graph algorithms (shortest paths, flows), such as offered in Algoritmiek, and NP-completeness, such as offered in Algoritmiek or Algorithms for Decision Support. Having taken Algorithms and Networks is very helpful, but not required.
Literature:May change!
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.
Course form:The first part of the course will have two lectures a week and a tutorial. This part focuses mostly on mathematical models. The second part consists of writing a term paper, peer reviewing, and a flash talk.

**New in 2020**
The term paper can take two forms: a literature study or an experimental study.
The literature study will focus on recent papers in the literature on theoretical aspects of Network Science. Your term paper will discuss one paper in detail and provide insights on a few others.
The experimental project will focus on challenges in community detection. Your term paper will describe the implemented algorithms and compare them.
Exam form:Exam on studied chapters of the book, term paper, presentation, peer review. See the course webpage for details.
Minimum effort to qualify for 2nd chance exam:To qualify for the retake exam, the grade of the original must be at least 4.