LEARNING OUTCOMES
This course will provide students with fundamentals of complex network theory, together with skills for applying this knowledge in to practical network analysis.
Credits: 5
Schedule: 04.09.2024 - 09.12.2024
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Jari Saramäki, Mikko Kivelä
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
Introduction to complex networks, fundamentals and basic concepts. Fundamental models: random networks, small-world networks, and scale-free networks. Network measures and analysis. Error and attack tolerance of networks, percolation, thresholds for epidemic spreading. Weighted networks. Social networks. Network inference. Clustering and communities. Multilayer and temporal networks.
Assessment Methods and Criteria
valid for whole curriculum period:
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.
Workload
valid for whole curriculum period:
9 lectures + 8 sets of weekly exercises + project work. Attending lectures or exercises sessions is not mandatory, but returning completed weekly exercise sets is.
DETAILS
Study Material
valid for whole curriculum period:
Lecture slides/video streams and references therein.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Autumn I - II
2025-2026 Autumn I - IIRegistration:
Students of Life Science Technologies have priority