Social Network Analysis for Computer Scientists

Vakbeschrijving Social Network Analysis for Computer Scientists
Collegejaar: 2018-2019
Studiegidsnummer: 4343SNACS
Docent(en):
  • dr. F.W. Takes
Voertaal: Engels
Blackboard: Nee
EC: 6
Niveau: 500
Periode: Semester 1
  • Wel Keuzevak
  • Geen Contractonderwijs
  • Wel Exchange
  • Wel Study Abroad
  • Geen Avondonderwijs
  • Geen A-la-Carte en Aanschuifonderwijs
  • Geen Honours Class

Admission requirements

Knowledge of Algorithms, Data Structures and Data Mining (for example, Algoritmiek, Datastructuren and Data Mining from the Leiden Informatica BSc programme).

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.

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, see course website: SNACS.

Literature:

  • Provided papers (no book).

Mode of instruction

  • Seminar
  • Individual and team assignments

Assessment method

Homework assignments and course project.

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 information

Lecturer: dr. Frank Takes
Website: SNACS

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