Social Networks of Shakespearean Plays – part 2

As a follow on to my previous work about social networks in Shakespeare, I wanted to see how the social network changes throughout the course of the play.  Using the same techniques as last time we can look at the social network structure by act.

The density of the social network underlies the style of the storytelling.  Movie Galaxies (the site that originally inspired this work) has found some striking differences between different directors by looking at this network feature.

Let’s start off with two extreme examples, “A Midsummer Nights’ Dream” and “Macbeth”  “The Scottish Play.”  From left to right we proceed through the five acts.  We’re showing the networks defined by being in at least one scene together.

 A Midsummer Night’s Dream

 Macbeth

 

We see a striking difference between the structure of the networks. Dream’s network is dense and clustered.  Macbeth is more spread out, indicative of the various malicious plotting of the play.  We can quantify this observation by plotting the network density of the two plays by act:But how does this look in the aggregate?  Are there recurring patterns of social networks between the different genres in the folios?  If we plot the average network density by act of the comedies vs. the tragedies we see, indeed, there are (for the scientifically inclined, error bars are standard error of mean)

Comedy vs. Tragedy, social network density by act

I draw two observations from this:

  1. The comedies are the plays that end with a wedding, and literally everyone’s invited.
  2. In Act IV of a comedy, there’s subplot.  In Act IV of a Tragedy, there’s subplot-ing-to-kill-you.  If you happen to find yourself in Act IV of a Shakespearean tragedy don’t trust anyone that’s off stage (although, also mind anyone that is sharing your scene, especially if they are carrying a poisoned sword).

If you’re interested in playing with some of these ideas, I’ve posted the python code I’ve used to generate the social networks and some of the analysis on GitHub as well as my XML formatted Shakespeare.  It is admittedly (very) rough code.  I have intentions of going back and cleaning it up, but thought I’d post it now, just in case someone had an interest in playing around with it.

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