Aim and approach

This course provides a strong methodology foundation in applying network analysis methods for social sciences. In the end of the course, the student knows how to (a) conceptually apply networks in work and (b) knows how to code network analysis using R or Python language.

This course focuses on

  • conceptual understanding of applying network analysis approaches through discussing papers which have used a particular approach.
  • hands-on skills of conducting these analysis with data sets

If you have questions about the course, please contact me via or come to my office hours.


Before the first class, student should master basics or Python or R and know how to

  • working with variables, for-loops and if-structures
  • opening files and writing files
  • calling functions or methods

Course materials

  • Computational Thinking and Social Science (shared via email)
  • In class coding activities, install needed packages from Setup Python or Setup R

Course evaluation

Course is evaluated as pass/fail.

To pass, you need writing a manuscript using some of the methods and techniques presented during the course. The manuscript must be written during the Spring semester. Please use office hours to gain more feedback and comments on your manuscript. The manuscript must have all bits in place: there must be a meaningful introduction, some theory or previous work section, description of data and methods, and a discussion.


9.1. Day one

9.15-10.00: Introduction

Read before the class

  • Chapter "Network analysis" from Computational Thinking and Social Science

Supplementary readings

  • Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network Analysis in the Social Sciences. Science, 323(5916), 892–895. link
  • Garton, L., Haythornthwaite, C., & Wellman, B. (1997). Studying Online Social Networks. Journal of Computer-Mediated Communication, 3(1), 0–0. link

10:00 - 14:00 Descriptive network analysis

Read before class

  • Cross, R., Borgatti, S. P., & Parker, A. (2002). Making Invisible Work Visible: Using Social Network Analysis to Support Strategic Collaboration. California Management Review, 44(2), 25–46. link

Code exercise

  • Topic 1
  • Topic 2

11:00-12:00 Lunch break

14:00 - 15:45 Network as a paradigm

Read before class

  • Hokka, J., & Nelimarkka, M. (2020). Affective economy of national-populist images: Investigating national and transnational online networks through visual big data. New Media & Society link

Code exercise

  • Topic 6

Supplementary readings

  • Hellsten, I., & Leydesdorff, L. (2019). Automated analysis of actor–topic networks on Twitter: New approaches to the analysis of socio‐semantic networks. Journal of the Association for Information Science and Technology, 00(0), asi.24207. link
  • Karikoski, J., & Nelimarkka, M. (2010). Measuring Social Relations: Case OtaSizzle. In 2010 IEEE Second International Conference on Social Computing (pp. 257–263). IEEE. link
  • Wu, Y., Pitipornvivat, N., Zhao, J., Yang, S., Huang, G., & Qu, H. (2016). egoSlider: Visual Analysis of Egocentric Network Evolution. IEEE Transactions on Visualization and Computer Graphics, 22(1), 260–269. link

10.1. Day two

9.15 - 11:00 Analysis of networks

Read before class

  • Goodreau, S. M., Kitts, J. A., & Morris, M. (2009). Birds of a feather, or friend of a friend? Using exponential random graph models to investigate adolescent social networks. Demography, 46 (1), 103–125. link

Code exercise

  • Topic 3

11-12 Lunch break

12:00 - 14:30 Algorithmic analysis of networks

Read before class

  • Himelboim, I., McCreery, S., & Smith, M. (2013). Birds of a Feather Tweet Together: Integrating Network and Content Analyses to Examine Cross-Ideology Exposure on Twitter. Journal of Computer-Mediated Communication, 18(2), 40–60.

Code exercise

  • Topic 5

Supplementary readings

  • Read Wikipedia on community detection algorithms.
  • Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582. link
  • Leskovec, J., Lang, K. J., & Mahoney, M. (2010). Empirical comparison of algorithms for network community detection. Proceedings of the 19th International Conference on World Wide Web - WWW ’10, 631. link
  • Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. (2016). Quantifying Controversy in Social Media. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining - WSDM ’16 (pp. 33–42). New York, New York, USA: ACM Press. link

14.30 - 15:45 Conversations on ethics and reliability

Read before class

  • Chapter "Research Ethics in Computational Social Science" from Computational Thinking and Social Science
  • Chapter "Mistakes and Quality of Results in Computational Social Sciences" from Computational Thinking and Social Science

20.1. Project work kickoff

Read before class

  • Chapter "Integrating Computational Methods into Research" from Computational Thinking and Social Science

9.15 - 12:00 Project planning activities

12:00 - 13:00 Break

13:00 - 15:45 Drop-in hours for individual support