Spring 2020

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.

Course is taught by Matti Nelimarkka

  • Office hours: book a slot in office hours
  • For other inquiries, use grp-dcm-teaching@helsinki.fi

Course schedule

University of Helsinki student registration

Other students

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 in an empirical data and case, as well as working out the exercises given in the classroom.

Final work deadline is May 31sth, 2021. We will organise milestones to structure the process towards this deadline.

Before first class

  1. Read Chapter 4: Network analysis from Coding Social Science (link provided via email)
  2. Install Anaconda 3.7 to your laptop. (Or, if you are more familiar with other R/Python tools, those.)
  3. 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.

Materials

Course syllabus

Topic 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

Topic 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 (required reading)

Key terms

  • plotting graphs

Topic 3. Analysis of networks

Key terms

  • random graph model

Topic 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 (required reading)

Key terms

  • bipartite network

Topic 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 (required reading)

Key terms

  • clusters
  • random walks
  • morphology

Topic 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

Topic 7. Research design

  • data collection

Schedule

Monday Tuesday
9
Introductions Topic 5: discussion: What can I do with algorithms for network analysis?
Chapter recap exercise Topic 5: Lab exercises
Chapter recap exercise Topic 5: Lab exercises
10 Defining key terms for topics 1 & 2 Topic 6: discussion: Why to move beyond simple networks
Code lab: ”Topic 1 and 2” Topic 6: Lab exercises
Code lab: ”Topic 1 and 2” Topic 6: Lab exercises
Code lab: ”Topic 1 and 2” Break
11 Topic 2 in-class exercise Topic 7: presentation
Topic 2 in-class exercise Topic 7: presentation
Topic 2 in-class exercise Topic 7: discussion
Break Wrapup Day 2 and instruction to individual projects
12 Break Wrapup Day 2 and instruction to individual projects
Break Break (Matti available for 1-to-1 -discussions)
Topic 3 discussion: What are RGM and why should I use them? Break (Matti available for 1-to-1 -discussions)
Topic 3 code lab Break (Matti available for 1-to-1 -discussions)
13 Break Time to work on individual project (Matti is not available)
Topic 4: In-class exercise: Network as a paradigm Time to work on individual project (Matti is not available)
Topic 4: In-class exercise: Network as a paradigm Time to work on individual project (Matti is not available)
Topic 4: In-class exercise: Network as a paradigm Time to work on individual project (Matti is not available)
14 Break Time to work on individual project (Matti is not available)
Discussing empirical case papers Time to work on individual project (Matti is not available)
Discussing empirical case papers Time to work on individual project (Matti is not available)
Discussing empirical case papers Time to work on individual project (Matti is not available)
15 Wrap-up: day 1 Time to work on individual project (Matti is not available)
Wrap-up: day 1 Time to work on individual project (Matti is not available)
Voluntary social hour Submission of individual project plans and schedules.
Voluntary social hour