Week 5 Blog Post: Marvel

If you can’t see the visualization, here is the link

My visualization today is a network graph that shows the alignment of characters in the Marvel Universe. The Marvel data I used to make this visualization categorizes characters into four categories: Good Characters, Bad Characters, Neutral Characters, and [No Alignment] (aka the box was left blank). It is a simple network visualization, as the nodes cluster around only four different labels, with no connections between the clusters (therefore, it is not a bipartite network). For the purpose of this analysis, I’m going to be ignoring the [No Alignment] cluster, since my group still needs to find out why there was no alignment assigned to them.

From this data set, it is easy to see that Marvel definitively divides characters into Good/Bad/Neutral, with no overlap occurring. The same can be said for the partner DC data we were given, and is not a surprise considering that is what is most easily marketed to a vast array of audiences. Since they are all isolated into singular clusters, with no edges connecting to more than one vertice, it is very clear that the characters are presented by Marvel as only having one possible attribute. However, this leaves out a lot to the viewers, especially those who are not die-hard fans of the company.  We are given no reasons why particular characters have the alignments they do, and whether or not those alignments have been consistent. Some questions that had come up when looking at this data visualization were “Are their alignments static? When were these recorded?”, as there is no way to tell until we look further into this dataset and the history behind it.

To put it a little in perspective, when running the DC Comics data (formatted exactly the same as the Marvel data), you see a node pop up that says “Reformed Criminals”. I’ve inserted a screenshot below:

Therefore, one questions whether or not Marvel has those types of characters present in their universe at all, or if their characters, once typecast, are then forever labelled and characterized as good, evil, or neutral.

The reason why I chose to use a network diagram for my visualization is to show that these edges are clustered into isolated nodes, and that even Bad Characters, who made the transition into Good Characters, get isolated under the term of “Reformed Criminals”. Maybe, for the sake of having cleaner, more understandable data, they have been put into these binary-type labels, as a spectrum of labels would make it more difficult to draw meaningful conclusions from. Having a spectrum of labels, although it makes the narrative of the individual clearer, does tend to obscure the overall narrative of the collective data.