5.3 Assignments and Activities

Due by Monday, 2/25, 11:59PM

5.3.1 Read and build knowledge

Read Carolan (2014) Chapter 5. Make Hypothesis annotations as we normally do using proper hashtags (e.g., question, idea). Don’t forget to include our SNAEd course hashtag.

Build knowledge as a group: When reading through the chapter, identify at least one network-level measure, conduct research on how it could be computed using R or Gephi, and share your solution using Hypothesis. Include at least three hashtags in your Hypothesis annotation: SNAEd, compute and R/Gephi. - If you found someone else has already covered your ‘favorite’ measure, try to build on the existing solution by, for example, introducing a parameter/mechanism to deal with weighted networks.

Finally, if you find a network-level measures not captured in the book, please annotate HERE on this page. Briefly introduce the measure and provide a computation technique.

5.3.2 Assignment: Data collection hands-on

Last week, we used the Les Miserables sample dataset. This week, you are encouraged to identify a dataset – public or your own – and organize it according to the “tidy data” principles discussed in Week 4.

  • The Network Data Repository is a good place to look at, or you can build a network based on public data (e.g., from a novel, Twitter). Take a look at NodeXL if you’re interested in public Twitter data.
  • You’re not expected to accomplish an SNA study by the end of this semester, but you are encouraged to work on an authentic study of your own when possible.
    • Please note that if you plan to conduct a study that involves human subjects, an IRB approval is often required. The IRB office’s webpage is a great starting point for you to decide whether you need an IRB approval.
  • If the public dataset you find is not tidy, you will need to transform it to a tidy dataset.

When submitting this assignment, depending on whether your dataset contains sensitive data or not, you can choose to share it publicly within the class on the assignment Slack channel, or only share computed results (more below) with the class on Slack.

5.3.3 Compute network-level measures and share

No matter which dataset you use, please try to compute network-level measures and share back to the class on Slack.

You are encouraged to share scripts (for Track R) or mini-videos (for Track Gephi and R) even though they are not required. You are also encouraged to ‘triangulate’ measures with visualizations.

Enjoy a great week!