Graphs with Palladio

This past week, we were revealed to the simplicity and complexity behind networks and the visualization of networks. Weingart’s article, “Demystifying Networks,” begins with a real simplification of a network: “a net-like arrangement of threads, wires, etc.” which later incorporated “stuff and relationships.” Moreover, network visualizations explore these relationships in many dimensions: degree, attributes, directionality, and more (Powerpoint about Social Networks).

As a class, we used Palladio to gain first-hand experience with creating network visualization. Using this sample data pertaining to Ralph Neumann, we utilized the Graph function of Palladio.

First, I set the Source to “Giver” and the Target to “Recipient.” Highlighting the “Giver,” however, was not useful, because it didn’t distinguish any of the dots, signifying that everyone was a Giver.

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Instead, I highlighted the Recipients. As a result, a few Recipient dots were highlighted, pointing out who received help.

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Then, I changed the Target to “Time Step Start” and checked “size nodes,” and the following was revealed.

who gave help and when

Here, we can easily see the amount of people that were helped according to who is connected to the larger center dot with a number. However, here we have a questionable large dot on the bottom that has no links.

Still playing with Sources and Targets, I changed the Source to “Recipient.” This graph visualizes who was present to receive help at the same moment of time. But again, we have a dot at the top without any information. This may indicate that there is missing data in our dataset.

visualizes who was present to receive help at the same moment in time

Lastly, I experimented with the Timespan filter. This timespan allows us to limit the graph to only visualize those who received help within this period “0012-01-1 and 0012-12-29.”

Screen Shot 2015-11-05 at 2.26.17 PM

Experimenting with Palladio raised a lot of concerns for me; one being, I should definitely know my data before plugging it into a tool. I am not familiar with how the data was collected or what the numbers represent, which limit my ability to make any conclusions. Secondly, I would prefer if there were more visual options to customize, such as color of nodes or adding arrows to signify direction. Lastly, all the images pasted here are screenshots throughout the process. The download option saves as a .json file that can be reopened in Palladio but not reposted onto a blog like this. A screenshot does not do Palladio justice, because resizing impedes the lengths of lines and size of the font. All in all, it is a groundbreaking tool in network visualization, definitely considering that the data uploaded will not be datamined for other purposes.

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