Credits: 5
Schedule: 10.01.2019 - 14.02.2019
Teaching Period (valid 01.08.2018-31.07.2020):
III (Spring)
Learning Outcomes (valid 01.08.2018-31.07.2020):
This course will provide students an overview on mathematical topics and tools in network science. The students will develop skills in doing pen-and-paper calculations and an understanding of analytical methods that are common in network science.
Content (valid 01.08.2018-31.07.2020):
Mathematical methods and their use in networks science. Topics are mostly related to large random graphs and include common approximations and assumptions in network science, network models, component size distributions, percolation, branching processes, excess degree distributions, probability generating functions, master equations, rate equations, growing network models, processes on networks, exponential random graphs, and stochastic block models. The project can be on a recent research topic.
Assessment Methods and Criteria (valid 01.08.2018-31.07.2020):
Grades (1-5) are given on the basis of weekly mandatory exercises and project work, there is no exam.
Prerequisites (valid 01.08.2018-31.07.2020):
Basic mathematics courses, basics of network science (CS-E5740 Complex Networks), basics on stochastic processes (MS-C2111 Stochastic Processes).
Grading Scale (valid 01.08.2018-31.07.2020):
0-5
- Teacher: Badie-Modiri Arash
- Teacher: Kivelä Mikko