Social Network Analysis

Course content

Social Network Analysis (SNA) has gained popularity in many different fields, ranging from political science to economy. Today, the approach is also an important part of social data science. SNA explores the relations between entities (politicians, firms, countries, etc.), while entailing both different methods and underlying social theories. This seminar will introduce core concepts and topics in SNA, including network structure, centrality and communities. The students will learn how to develop relevant research questions, collect data and to analyze networks using the programming language “R”. Furthermore, the seminar offers hands-on experience with analysis of political networks on social media, while also being relevant to those who wish to use SNA for other purposes.

Education

Full-degree students enrolled at the Department of Political Science, UCPH

  • MSc in Political Science
  • MSc in Social Science
  • MSc in Security Risk Management
  • Bachelor in Political Science

 

Full-degree students enrolled at the Faculty of Social Science, UCPH 

  • Master Programme in Social Data Science
  • Bachelor and Master Programmes in Sociology
  • Bachelor and Master Programmes in Psychology

 

The course is open to:

  • Exchange and Guest students from abroad
  • Credit students from Danish Universities
  • Open University students
Learning outcome

Knowledge:

  • Describe relevant concepts and theories
  • Understand how theoretical concepts in social network theory can be applied in empirical research
  • Reflect upon the limitations of relevant theoretical concepts and methods

 

Skills:

  • Critically discuss empirical work
  • Analyze networks in R
  • Visualize networks using Gephi and/or other software

 

Competences:

  • Develop research questions
  • Independently plan and conduct research relate to social networks

The class consists of lectures and practical sessions, where the course participants will solve tasks in R. The students will prepare for class by reading the assigned material prior to the sessions. In some instances, course participants will be advised to run code in R prior to the lectures.
The course strongly depends on active student participation in class. The students will use groups to solve practical tasks and to shape their own research project for the written assignment.

Burt, R.S., 2004. Structural holes and good ideas. American journal of sociology, 110(2), pp.349-399.

Diani, M. and McAdam, D. eds., 2003. Social movements and networks: Relational approaches to collective action. Oxford University Press.

Emirbayer, M., 1997. Manifesto for a relational sociology. American journal of sociology, 103(2), pp.281-317.

Freeman, L.C., 1978. Centrality in social networks conceptual clarification. Social networks, 1(3), pp.215-239.

Granovetter, M.S., 1973. The strength of weak ties. American journal of sociology, 78(6), pp.1360-1380.

Lazer, D., Pentland, A.S., Adamic, L., Aral, S., Barabasi, A.L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M. and Jebara, T., 2009. Life in the network: the coming age of computational social science. Science (New York, NY), 323(5915), p.721

Lin, N., 1999. Building a network theory of social capital. Connections, 22(1), pp.28-51

Scott, J., 2017. Social network analysis. Sage.

It is strongly recommended that the students have at least minimal experience with either R or Python prior to the course. Those with no prior experience in R are advised to take a short online tutorial prior to the first lecture.

Peer feedback (Students give each other feedback)
ECTS
7,5 ECTS
Type of assessment
Written examination
Type of assessment details
Free written assignment
Marking scale
7-point grading scale
Censorship form
No external censorship
Criteria for exam assessment
  • Grade 12 is given for an outstanding performance: the student lives up to the course's goal description in an independent and convincing manner with no or few and minor shortcomings
  • Grade 7 is given for a good performance: the student is confidently able to live up to the goal description, albeit with several shortcomings
  • Grade 02 is given for an adequate performance: the minimum acceptable performance in which the student is only able to live up to the goal description in an insecure and incomplete manner

Single subject courses (day)

  • Category
  • Hours
  • Class Instruction
  • 28
  • English
  • 28

Kursusinformation

Language
English
Course number
ASTK18186U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Bachelor
Duration

1 semester

Placement
Autumn
Studyboard
Department of Political Science, Study Council
Contracting department
  • Department of Political Science
  • Department of Psychology
  • Social Data Science
  • Department of Sociology
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Yevgeniy Golovchenko   (2-87754e7774813c79833c7279)
Saved on the 16-05-2023

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