Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
This course will provide students with fundamentals of complex network theory, together with skills for applying this knowledge in to practical network analysis.
Schedule: 09.09.2020 - 09.12.2020
Teacher in charge (valid 01.08.2020-31.07.2022): Mikko Kivelä, Jari Saramäki
Teacher in charge (applies in this implementation): Mikko Kivelä, Jari Saramäki
Contact information for the course (valid 25.08.2020-21.12.2112):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Introduction to complex networks, fundamentals and basic concepts. Fundamental models: random networks, small-world networks, and scale-free networks. Network measures and analysis. Percolation theory. Weighted networks. Social networks. Network inference. Clustering and communities. Multilayer and temporal networks.
Assessment Methods and Criteria
Grades are given on the basis of weekly exercises and project work, there is no exam. Exercises and project work require Python programming, but you do not need to be familiar with Python beforehand, as long as you have some programming skills.
9 lectures + 8 sets of weekly exercises + project work. Attending lectures or exercises sessions is not mandatory, but returning completed weekly exercise sets is.
Lecture slides/video streams and references therein.
Substitutes for Courses
Replaces the former course BECS-114.4150 Complex Networks.
Some programming skills are required; the weekly exercises will be done with Python.