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Studiegids

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Social Network Analysis for Computer Scientists

Vak
2020-2021

Admission requirements

Recommended prior knowledge

It is recommended that students have knowledge of algorithms, data structures and data mining (see for example the content of the Algorithms, Data Structures and Data Mining courses in the Leiden BSc programme in Computer Science).

Description

This course deals with the computer science aspects of social network analysis. With topics such as big data and data science becoming increasingly popular, the study of large datasets of networks (or graphs), is becoming increasingly important. Examples of such networks include webgraphs, communication and collaboration networks and perhaps most notably (online) social networks (such as Facebook and Twitter). With millions of nodes and possible billions of links, traditional graph algorithms are often too complex and unable to solve trivial algorithmic and data mining related problems. Typical tasks in this field include clustering, outlier detection, link prediction but also more fundamental problems such as efficient retrieval, storage, and compression of graph data and computational problems such as computing shortest paths and other descriptive graph properties.

It is recommended that students have knowledge of algorithms, data structures and data mining (see for example the content of the Algorithmics, Data Structures and Data Mining courses in the Leiden BSc programme in Computer Science).

Course objectives

At the end of this course, students should:

  • Have a clear understanding of the state of the art of computer science aspects of social network analysis (“the field”).

  • Be sufficiently skilled to understand, implement and run algorithms for large graphs using self-written code or existing open source software packages.

  • Be able to perform experiments on large graphs in order to verify the performance of techniques for solving typical computer science related problems from the field.

  • Have the skills to compare different types of algorithms using quantitative measures common in the field.

  • Be able to write a scientific paper in which one or more algorithms from the field are described, analyzed and compared.

Timetable

The most recent timetable can be found at the students' website.

For a table of contents and all other course information, see course website: SNACS.

Mode of instruction

  • Lectures

  • Seminars

  • Individual assignments

  • Team project

Course load

Total hours of study: 168 hrs. (= 6 EC)
Lectures: 28:00 hrs.
Assignments: 56:00 hrs.
Project (presenation, programming, paper): 84:00 hrs.

Assessment method

Homework assignments and course project.

The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.

Reading list

Provided papers (no book).

Registration

You have to sign up for courses and exams (including retakes) in uSis. Check this link for information about how to register for courses.

Contact

Lecturer: dr. Frank Takes
Website: SNACS

Remarks

None.