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ID2224 Networks in Data Science 7.5 credits

Administer About course

Course offerings are missing for current or upcoming semesters.
Headings with content from the Course syllabus ID2224 (Autumn 2016–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • 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.

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

No information inserted

Recommended prerequisites

Basic knowledge in distributed systems (ID2203 and ID2210). Acquaintance with concepts and terminology associated with linear algebra, statistics, probability theory.

Equipment

No information inserted

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

A, B, C, D, E, FX, F

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

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Profile picture Sarunas Girdzijauskas

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

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

ICT/Software and Computer system

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

No information inserted

Supplementary information

Thios course will never be given. The material will be dsitributed to other courses.