Spring 2019

Course details

The course combines both theoretical knowledge about network analysis and hands-on skills on how to conduct such analysis.

This course focus both on the theory and practice of network analysis, perceived broadly as an analysis paradigm to understand interactions between people (social networks), relationships between words in the text (text networks) and so on. As this course has both lectures on more methodological nature and hands-on-laboratories, you should have a working knowledge of R or Python before the first class.

More details (class times, enrollment etc.): see University of Helsinki course website.

Course is taught by Matti Nelimarkka. Office hours, see Matti's website.

Learning objectives and evaluation

The course is evaluated as pass/fail. This is to encourage students to engage with the class without risks to their GPA.

Passing grade requires submitting a final project work using class concepts and techniques to an empirical data.

Before first class

  1. Recap Python or R. You should be able to
    • load a files to program and create a data frame (R) or extract variables (Python) from it.
    • basic manipulations to numeric data (sum, division, etc.) and text (splitting text, combining text)
    • write files
    • use functions
    • You can use for example CodeAcademy or DataCarpentry to fresh you your skills.
  2. Install Anaconda 3.7 to your laptop.

During each class

  • Before the class, read the assigned texts for each class.
  • Bring your own laptop with you.

  • We will spent about one hour of the class to discuss the conceptual bits and another hour working on lab exercises.


Course syllabus

1. What is a network?

  • 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

Key terms

  • adjacency list
  • directed graph
  • degree
  • centrality
  • density

2. Descriptive network analysis

  • 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

Key terms

  • plotting graphs

3. Analysis of networks

Key terms

  • random graph model

4. Network as a paradigm

  • 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

Key terms

  • bipartite network

5. Algorithmic analysis of networks

  • Read Wikipedia on community detection algorithms.
  • 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

Key terms

  • clusters
  • random walks
  • morphology

6. Beyond simple networks

  • 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

Key terms

  • multilayered networks
  • temporal networks
  • words as networks

7. Research design

  • data collection