TSKS34 · 6 credits · Basic level (G2F) · Datateknik, Datavetenskap, Elektroteknik
Complex Networks
Active · next start VT 2027
58%
passed the latest ordinary exam
TEN1 · 3 Jun 2026 · 26 students
77%
passed the ordinary exams, all sittings over the last 5 years
TEN1 · 62 students
Unusually hard in 2025/26
58% passed the ordinary exam in 2025/26 (26 students), against 92% on average in earlier years (36 students).
Part of 1 programme
Exam
TEN1 Written Examination
- 5
- 4
- 3
- U
Ordinary exams
77% passed · 62 students
- 58%26
- 94%18
- 89%18
Re-exams
100% passed · 12 students
- 100%12
- Pass the re-exam: 100% (12 students), compared with 77% (62 students) on the ordinary exam
- Get 4 or 5: 53% (74 students)
Show small sittings (3)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%2
- 100%1
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB1 Laboratory Sessions
- G
Main rounds
100% passed · 64 students
- 100%24
- 100%20
- 100%20
Later rounds
No sittings with at least 5 students.
Show small sittings (1)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%3
About the course
Aim
After completing the course the students should
- with adequate terminology, in a well-structured manner and logically coherent, be able to describe and conduct simpler calculations that relate to the specific concepts listed under "course contents".
- be able to describe, apply, and implement in a conventional programming language, and show engineering understanding of the theory and models used in the course.
- be able to, in a structured manner, and using adequate language and terminology, orally report computer laboratory work.
Content
Graph representation of networks, adjacency matrix, degree sequence and degree distribution. Walks, paths and motifs. Laplacian and introductory spectral graph theory. Signed, weighted, and bipartite networks. Similarity metrics and clustering coefficients. Centrality metrics. Sampling on graphs, random walks, friendship paradoxes. Metrics of assortativity, modularity and degree correlations. Algorithms for partitioning and community detection: Kernighan-Lin, Girvan-Newman and spectral algorithms. Basic network models: Poisson random graphs, configuration model, and preferential attachment. Dynamic models and diffusion. Sustainability aspects of data science and AI.
- Examiner
- Danyo Danev
- Scheduled hours
- 54 h
- Main field of study
- Datateknik, Datavetenskap, Elektroteknik