Open-ended Network Analysis

Students pick a dataset, extract a network from it, then write a report describing it.


Students pick a dataset, extract a network from it, then write a report describing it. They must write about what they expected the network to look like and discuss the differences with the actual network. They conclude with new questions for future work. They must also provide the network file (Gephi or similar).

Learning Outcomes
By the end of this assignment, students will be able to:
  1. Demonstrate creating and describing networks (nodes and edges) from a data set
  2. Demonstrate critical inquiry of networks (forming hypotheses and assessing them)

This assignment assumes students are familiar with networks and software to visualize networks. Depending on the dataset they choose, they should also be capable of extracting networks from data (this can be avoided if the student chooses a dataset that is already in an appropriate format).

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CSC440 Data Mining & Visualization, Spring 2019



This was the first run of the assignment. Class instructions was spent on network basics, extracting networks, and visualizing networks. Very little class time was devoted to analyzing networks. No models (good or bad) of the report were provided to stuents.

Outcome summary

The analyses were generally shallow and not particularly interesting. Most of the visualizations were underwhelming as well—it was difficult to extract much information out of them. The next time I give this assignment, I would have students select a dataset and then formulate and submit a hypothesis as to what they expect the network to look like a week or more prior to the deadline. I would also provide them models of what reasonable hypotheses and analyses are. We focused most of our time in class on the technical aspects and less so on methods of critical inquiry of networks. In terms of presentation, I would spend some class time on appropriate ways of embedding network diagrams in writing, such as overlaying annotations to draw attention to key features of the network and zooming in on interesting parts.



From the web

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