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Complex networks and big data, 6 credits
Komplexa nätverk och stora datamängder, 6 hp
TSKS33
Main field of study
Information Technology Computer Science and Engineering Computer Science Electrical EngineeringCourse level
Second cycleCourse type
Programme courseExaminer
Erik G. LarssonDirector of studies or equivalent
Lasse AlfredssonEducation components
Preliminary scheduled hours: 54 hRecommended self-study hours: 106 h
Available for exchange students
YesECV = Elective / Compulsory / Voluntary
Main field of study
Information Technology, Computer Science and Engineering, Computer Science, Electrical EngineeringCourse level
Second cycleAdvancement level
A1XCourse offered for
- Master of Science in Computer Science and Engineering
- Master of Science in Industrial Engineering and Management
- Master of Science in Industrial Engineering and Management - International
- Master of Science in Information Technology
- Master of Science in Applied Physics and Electrical Engineering
- Master of Science in Applied Physics and Electrical Engineering - International
- Bachelor's Programme in Mathematics
- Master of Science in Computer Science and Software Engineering
- Master's Programme in Mathematics
- Master's Programme in Communication Systems
Specific information
Replaces TSKS11.
Prerequisites
Linear algebra. Basic knowledge and understanding of probability theory/statistics. Programming skills in Python and Matlab.
Intended learning outcomes
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.
Course content
Introduction to complex networks and network science. Graph representation of networks, adjacency matrix, degree sequence and degree distribution. Walks, paths and network motifs. Laplacian and its properties. Signed networks, bipartite, affiliation and tripartite networks. Similarity and clustering metrics. Centrality metrics, eigenvector centrality, Katz, PageRank, hubs and authorities. Sampling of networks, random walks, and friendship paradoxes. Assortativity metrics, modularity and degree correlations. Community detection and partitioning: Kernighan-Lin, Girvan-Newman and spectral algorithms. Network formation models: Poisson random networks, configuration model, preferential attachment, power-laws and scale-free networks, cutoffs. Watts-Strogatz model, Kleinberg model, small-world phenomena, searchability and reachability. Cascades, linear threshold models, DeGroot dynamic models and diffusion. Introduction to graph learning and graph signal processing.
Teaching and working methods
The course consists of 12 lectures, 7 tutorials and a series of computer laboratories. In-class examination of the computer laboratory work.
Examination
| TEN1 | Written examination | 4 credits | U, 3, 4, 5 |
| LAB1 | Laboratory work | 2 credits | U, G |
Grades
Four-grade scale, LiU, U, 3, 4, 5Other information
Supplementary courses: Courses in computer, information and communication networks, Internet and web technology, social networks, graph theory, machine learning and network analysis.
About teaching and examination language
The teaching language is presented in the Overview tab for each course. The examination language relates to the teaching language as follows:
- If teaching language is Swedish, the course as a whole or in large parts, is taught in Swedish. Please note that although teaching language is Swedish, parts of the course could be given in English. Examination language is Swedish.
- If teaching language is Swedish/English, the course as a whole will be taught in English if students without prior knowledge of the Swedish language participate. Examination language is Swedish or English (depending on teaching language).
- If teaching language is English, the course as a whole is taught in English. Examination language is English.
Other
The course is conducted in a manner where both men's and women's experience and knowledge are made visible and developed.
The planning and implementation of a course should correspond to the course syllabus. The course evaluation should therefore be conducted with the course syllabus as a starting point.
Department
Institutionen för systemteknikDirector of Studies or equivalent
Lasse AlfredssonExaminer
Erik G. LarssonEducation components
Preliminary scheduled hours: 54 hRecommended self-study hours: 106 h
Course literature
Regulary literature
Books
- Latora, Vito, Nicosia, Vincenzo, Russo, Giovanni, (2017) Complex networks : principles, methods and applications Cambridge : Cambridge University Press, 2017.
ISBN: 9781107103184, 1107103185, 9781108299961
Additional literature
Compendia
Supplementary notes by E. G. Larsson.
| Code | Name | Scope | Grading scale |
|---|---|---|---|
| TEN1 | Written examination | 4 credits | U, 3, 4, 5 |
| LAB1 | Laboratory work | 2 credits | U, G |
Regulary literature
Books
Latora, Vito, Nicosia, Vincenzo, Russo, Giovanni, (2017) Complex networks : principles, methods and applications Cambridge : Cambridge University Press, 2017.
ISBN: 9781107103184, 1107103185, 9781108299961
Additional literature
Compendia
Supplementary notes by E. G. Larsson.
Note: The course matrix might contain more information in Swedish.
I = Introduce, U = Teach, A = Utilize
| I | U | A | Modules | Comment | ||
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| 1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
| 1.1 Knowledge of underlying mathematics and science (G1X level) |
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| 1.2 Fundamental engineering knowledge (G1X level) |
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| 1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level) |
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| 1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level) |
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| 1.5 Insight into current research and development work |
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| 2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
| 2.1 Analytical reasoning and problem solving |
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| 2.2 Experimentation, investigation, and knowledge discovery |
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| 2.3 System thinking |
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| 2.4 Attitudes, thought, and learning |
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| 2.5 Ethics, equity, and other responsibilities |
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| 3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
| 3.1 Teamwork |
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| 3.2 Communications |
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| 3.3 Communication in foreign languages |
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| 4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
| 4.1 External, societal, and environmental context |
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| 4.2 Enterprise and business context |
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| 4.3 Conceiving, system engineering and management |
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| 4.4 Designing |
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| 4.5 Implementing |
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| 4.6 Operating |
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| 5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
| 5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development |
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| 5.2 Economic conditions for knowledge development |
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| 5.3 Identification of needs, structuring and planning of research or development projects |
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| 5.4 Execution of research or development projects |
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| 5.5 Presentation and evaluation of research or development projects |
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