Social Network Analysis

Spring 2024, Half-time, Norrköping

Semester Spring 2024
Place of study Norrköping
Pace of study Half-time
Level Second cycle
Teaching form On-Campus
Education Time Day-time
Education Language English
Course offering id LIU-46105
Period 202404 - 202413
Number of Places 2

Specific requirements

  • 180 ECTS credits passed including 90 ECTS credits in one of the following subject areas: social- and natural sciences, engineering, statistics, or mathematics
  • 15 ECTS credits in statistics, computer science, mathematics, or equivalent at advanced level
  • English corresponding to the level of English in Swedish upper secondary education (Engelska 6)
    Exemption from Swedish

Selection

Tuition fees

SEK 17600 - NB: Applies only to students from outside the EU, EEA and Switzerland.

If you have questions about the course, contact us

Károly Takács, course director

Madelene Töpfer, course administrator

Jonas Johansson, study adviser

Course syllabus

Social Network Analysis

Spring 2025, Half-time, Norrköping

Semester Spring 2025
Place of study Norrköping
Pace of study Half-time
Level Second cycle
Teaching form On-Campus
Education Time Day-time
Education Language English
Course offering id LIU-46105
Period 202504 - 202513
Number of Places 2

Specific requirements

  • 180 ECTS credits passed including 90 ECTS credits in one of the following subject areas: social- and natural sciences, engineering, statistics, or mathematics
  • 15 ECTS credits in statistics, computer science, mathematics, or equivalent at advanced level
  • English corresponding to the level of English in Swedish upper secondary education (Engelska 6)
    Exemption from Swedish

Selection

Tuition fees

SEK 17600 - NB: Applies only to students from outside the EU, EEA and Switzerland.

If you have questions about the course, contact us

Károly Takács, course director

Madelene Töpfer, course administrator

Jonas Johansson, study adviser

Course syllabus

This course presents key concepts, measures, and statistical techniques needed for the analysis of relational, social network data using a computational approach. Network concepts such as centrality and brokerage are discussed, and popular measures related to these concepts are reviewed. The course proceeds to computational methods for handling network data, producing network visualizations, and calculating relevant statistics. Statistical models applicable to network data are considered, and tutorials in relevant software tools are provided. Various statistical models for network data are presented and estimated in interactive computer labs involving real data, and methods for simulating network models are implemented.