Department of Information and Computing Sciences

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

Onderwijs Informatica en Informatiekunde

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Network science

Website:website containing additional information
Course code:INFOMNWSC
Credits:7.5 ECTS
Period:period 4 (week 17 through 26, i.e., 23-4-2018 through 29-6-2018; retake week 28)
Timeslot:A
Participants:up till now 34 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:
formgrouptimeweekroomteacher
lecture   Mon 11.00-12.4517-20 RUPPERT-B Erik Jan van Leeuwen
 
22-25 RUPPERT-B
Wed 9.00-12.4517 UNNIK-222
18 BBG-219
Wed 9.00-10.4519 RUPPERT-C
Wed 9.00-12.4520 BBG-219
Wed 9.00-10.4521 RUPPERT-C
Wed 9.00-12.4522-25 UNNIK-222
Wed 11.00-12.4519 UNNIK-222
21 UNNIK-222
Exam:
week: 26Mon 25-6-201811.00-13.00 uurroom: EDUC-BETA
week: 28Mon 9-7-201811.00-13.00 uurroom: BBG-001retake exam
Contents:Description
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 community detection and 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 potential topics
Basic algorithms for network science, lower bounds for polynomial-time problems, sampling algorithms, streaming algorithms, sublinear algorithms, power law algorithms, spreading phenomena, community detection, graph partitioning algorithms, phylogeny.

Prerequisites
The course assumes that you have basic skills in algorithms and mathematics. In particular, 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 helpful, but not required.
Literature:A. Barabasi, Network Science, for free online
Other relevant books and papers will be added on the course web page.
Course form:The first part of the course will have two lectures a week and a tutorial. The second part of the course will be a seminar with student presentations.
Exam form:Exam on studied chapters of the book, term paper, presentation. See the course webpage for details.
Minimum effort to qualify for 2nd chance exam:You must have given a presentation and a response. See the course webpage for details.
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