| TSKS11 |
Networks: models, algorithms and applications, 6 ECTS credits.
/Nätverk: modeller, algoritmer och tillämpningar/
For:
D
I
Ii
IT
Mat
MMAT
U
Y
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Prel. scheduled
hours: 56
Rec. self-study hours: 104
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Area of Education: Technology
Main field of studies: Eletrical Engineering
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Advancement level
(G1, G2, A): G2
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Aim:
After 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 graph representations of networks, metrics, centrality (Google PageRank, Katz, hub/authority, betweenness), structural balance, Laplace operator, random walks, spreading of information over networks, diffusion, cascades, models for and properties of random graphs, community detection, small-world phenomena, searchability and reachability, collaborative filtering on bipartite graphs (recommendation systems), power laws and information cascades
- be able to describe, apply, and implement in a conventional programming language, and show engineering understanding for the theory and models used in the course
- be able to report work in written form, using adequate language, terminology, structure and typography
- with adequate terminology, in a well structured way and with logical coherence, be able to describe the relations between different concepts taught in the course
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Prerequisites: (valid for students admitted to programmes within which the course is offered)
Linear algebra. Basic probability theory and optimization. Basic programming skills.
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshhold requirements for progression within the programme, or corresponding.
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Supplementary courses:
Courses in computer, information and communication networks, Internet and web technology, social networks, graph theroy, machine learning and network analysis.
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Organisation:
The course consists of 12 lectures, 6 tutorials and a series of computer laboratories. The work done in the computer laboratories is reported individually and in written form.
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Course contents:
Graph representations of networks, metrics, centrality (Google PageRank, Katz, hub/authority, betweenness). Structural balance. Laplace operator, random walks, spreading of information over networks. Diffusion. Cascades. Models for and properties of random graphs. Algorithms for analysis and partitioning of networks, community detection. Small-world phenomena, searchability and reachability. Collaborative filtering on bipartite graphs (recommendation systems). Power laws and information cascades.
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Course literature:
Networks: An introduction by M. Newman (compulsory), Networks, Crowds and Markets by D. Easley och J. Kleinberg (for supplementary reading) and selected material from Networked Life by M. Chiang
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Examination: |
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Written examination
Laboratory work
Laboratory work
Laboratory work
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2 ECTS
2 ECTS
1,5 ECTS
0,5 ECTS
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The exam (TEN1) is normally written, but the course director can decide to use oral examination, as complement to or as replacement for a written exam, in case there are few students taking the exam, or in other special cases. |
Course language is Swedish/English.
Department offering the course: ISY.
Director of Studies: Klas Nordberg
Examiner: Erik G. Larsson
Link to the course homepage at the department
Course Syllabus in Swedish
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