Social Network Analysis, 7.5 credits

Social nätverksanalys, 7.5 hp

771A23

Main field of study

Computational Social Science

Course level

Second cycle

Course type

Single subject and programme course

Examiner

Károly Takács

Course coordinator

Károly Takács

Director of studies or equivalent

Carl Nordlund

Contact

ECV = Elective / Compulsory / Voluntary
Course offered for Semester Weeks Language Campus ECV
F7MCD Computational Social Science, Master´s Programme - First and main admission round 2 (Spring 2022) 202204-202213 English Norrköping C
F7MCD Computational Social Science, Master´s Programme - Second admission round (open only for Swedish/EU students) 2 (Spring 2022) 202204-202213 English Norrköping

Main field of study

Computational Social Science

Course level

Second cycle

Advancement level

A1N

Course offered for

  • Master´s Programme in Computational Social Science

Entry requirements

A bachelor's degree or equivalent in the humanities, social-, cultural-, behavioural-, natural-, computer-, or engineering-sciences.
English corresponding to the level of English in Swedish upper secondary education (English 6/B).

Intended learning outcomes

After completing the course the student should at an advanced level be able to:

  • explain basic concepts and theories of network analysis in the social sciences, and understand how these concepts and theories can help explain different actors’ micro behaviors as well as macro outcomes;
  • critically examine the ways in which networks can contribute to the explanation of social, political, economic and cultural phenomena;
  • use statistical software to visualize networks and analyze their properties, connecting these to network concepts and theories;
  • explain principles underlying statistical models for social networks;
  • use software to implement statistical models of social networks to analyze network formation and evolution;
  • use software to simulate the dynamics of networks based on social network models.

 

Course content

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.

 

Teaching and working methods

The teaching consists of lectures, readings, computor labs, and seminars. Homework and independent studies are a necessary complement to the course.
Language of instruction: English

Examination

The course is examined through written assignments, active participation on seminars, computer labs, and a final written individual assignment.
Detailed information about the examination can be found in the course’s study guide. 

If special circumstances prevail, and if it is possible with consideration of the nature of the compulsory component, the examiner may decide to replace the compulsory component with another equivalent component.

If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it.

If the coordinator has recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.

An examiner may also decide that an adapted examination or alternative form of examination if the examiner assessed that special circumstances prevail, and the examiner assesses that it is possible while maintaining the objectives of the course.

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.

Grades

ECTS, EC

Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. 

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.

If special circumstances prevail, the vice-chancellor may in a special decision specify the preconditions for temporary deviations from this course syllabus, and delegate the right to take such decisions.

Department

Institutionen för ekonomisk och industriell utveckling
Code Name Scope Grading scale
ASS1 Assignments 4.5 credits EC
PRO2 Project 2 credits EC
LSP1 Literature seminar and presentations 1 credits EC

Books

Kolaczyk, Eric D., Csárdi, Gábor, (2014) Statistical analysis of network data with R. Springer, [2014]

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