- Main network models and their applications for P2P, Pub/Sub Systems
- Navigability in Structured and Unstructured Overlays
- Basics of Spectral Graph Theory
- Random Walks on Graphs
- Page Rank, Graph Clustering and Community detection, Social Network Analysis
- Algorithms for Massive Linked Data.
ID2224 Networks in Data Science 7.5 credits
This course has been discontinued.
Last planned examination: Spring 2021
Decision to discontinue this course:
No information insertedInformation per course offering
Course offerings are missing for current or upcoming semesters.
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus ID2224 (Autumn 2016–)Content and learning outcomes
Course contents
Intended learning outcomes
The students will after the course
- be able to summarize and describe the main network models and research solutions that are basis for building structured and unstructured P2P overlays and Publish/subscribe systems
- be able to summarize and describe the fundamental concepts of spectral graph theory and apply them in practice for graph topology analysis
- be able to summarize and describe the fundamental concepts of random walk theory and its practical applications on the link analysis of social networks and the web
- be able to elaborate on and apply algorithms for massive linked data problems (e.g., graph clustering, community detection etcetera).
Literature and preparations
Specific prerequisites
Recommended prerequisites
Basic knowledge in distributed systems (ID2203 and ID2210). Acquaintance with concepts and terminology associated with linear algebra, statistics, probability theory.
Equipment
Literature
The course is loosely based on the following books:
- John Hopcroft and Ravindran Kannan ” Foundations of Data Science” (2013)
- David Easley and Jon Kleinberg “Networks, Crowds, and Markets: Reasoning About a Highly Connected World” (2010)
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
Examination
- LAB1 - Programming Assignments, 3.0 credits, grading scale: P, F
- TEN1 - Examination, 4.5 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Written examination. Laboratory tasks.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Offered by
Main field of study
Education cycle
Add-on studies
Supplementary information
Thios course will never be given. The material will be dsitributed to other courses.