Course contents *

• Basic definitions in graph theory, strong and weak bands, grade distribution and clustering measurements.
• Erdos-Renyi, Wats-Strogatz, ccnfiguration models, the effect of a "small world".
• Random walks in graphs, Page Rank.
• Graph clustering, identification of "communities".
• The algorithm "Label Propagation", link prediction.
• Distributive semantics, topic modellering, document summary.

Intended learning outcomes *

After passing the course, the student shall be able to

• explain different fundamental concepts and algorithms in data mining and basic technologies for analysis and extraction in information networks (for example the fundamental concepts in graph theory, network models, algorithms left graph clustering, identification of "communities", "Label Propagation", link prediction, etc)
• analyse, choose, use, and evaluate technologies for data mining that is based on the above concepts and explore and implement the existing data mining algorithms independently
• communicate findings, results and ideas with clear and formal language.

Course Disposition

No information inserted

Specific prerequisites *

No information inserted

Recommended prerequisites

Familiarity with the basic probability theory, linear algebra as well as ability to write a non-trivial computer program.

Equipment

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Literature

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Grading scale *

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

Examination *

• PRO1 - Project, 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.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

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Examiner

Sarunas Girdzijauskas

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Offered by

EECS/Computer Science

Main field of study *

Computer Science and Engineering

Education cycle *

Second cycle

Add-on studies

No information inserted

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.

Supplementary information

In this course, the EECS code of honor applies, see: http://www.kth.se/en/eecs/utbildning/hederskodex.