|Website:||website containing additional information|
|Period:||period 4 (week 17 through 26, i.e., 22-4-2019 through 28-6-2019; retake week 28)|
|Participants:||up till now 58 subscriptions|
|Schedule:||Official schedule representation can be found in Osiris|
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 potential 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. 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|
M.E.J. Newman, Networks, 2nd edition (2018).
|Course form:||The first part of the course will have two lectures a week and a tutorial. The second part consists of writing a term paper, peer reviewing, and a flash talk.|
|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:||The 2nd chance exam requires at least a 4 in the first exam.|