ASTK18106U Social Network Analysis

Volume 2018/2019

Bachelor student: 10 ECTS

Master student: 7.5 ECTS


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.

Learning Outcome


  • 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



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



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


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.

Students are advised to have basic experience with statistical software or a programming language prior to the course (e.g. STATA, SPSS, R or Python)
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.
  • Category
  • Hours
  • Class Instruction
  • 28
  • Total
  • 28
Peer feedback (Students give each other feedback)
7,5 ECTS
Type of assessment
Written assignment
Free assignment
Marking scale
7-point grading scale
Censorship form
No external censorship

Free written assignment

Criteria for exam assesment
  • 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