Since this project’s beginning, I had toyed with the idea of doing a social network graph that would look at the relationships between all the characters in the novel. I was aware that this would be a substantially larger undertaking than any of the other visualizations I had in mind, which perhaps explains why I left it for last. Despite forewarning myself, I grossly underestimated how difficult that would be and set off to code character interactions over the course of 70 chapters in an 800 page novel. As an experience that opened up the novel to me in all sorts of new ways, it was wonderful. As a mix between skimming and data entry, it was profoundly unpleasant.
But enough lamenting the plight of the digital scholar, that’s boring. Here are the results:
Now for the specs. In order to create this graph, I needed to set some rules for what qualified as interaction. A bidirectional interaction occurs when one named character speaks aloud (that is, with quotation marks) to another named character. A unidirectional interaction occurs when a named character speaks aloud about another named character. The chart does not differentiate between two people who gossip about one another and two people who actually speak to one another. Also, the chart only shows the presence or absence of interaction, it does not add weight to the edges based on how many times interactions took place. I am aware that this is less than ideal, but as this is just my first foray into social network graphing, I have not yet worked out the full range of the software’s ability. I have the data to create that graph, just not the knowhow. But I plan to work it out when I have the chance.
Anyway, this graph was generated by the graphing software yEd. I told it to place the characters in a single circle and to use color to convey a character’s centrality (darker colored nodes have more connections to the other nodes). Then I just played around with the background because I am a sucker for light on dark presentation.
Here’s where it gets fun. I told the software to redraw the graph based on the groups it thought that the characters should be divided into (well, not in so many words, but that was how I translated the instructions in my head). The resulting graph is below.
Cool, right? The weirdest part, for me, was that Mrs. Davilow (Gwendolen’s mother) is at the center of the giant social cluster rather than Gwendolen herself. I have a few ideas as to why she might be–she’s more important than I tend to give her credit for–but I’m leery of creating post-hoc explanations for something that could simply be a software quirk. Still, it’s provocative.
The other point I want to make is about families. Here is another version of this graph, this time with immediate family members all colored the same color.
Now, it’s much easier to see which family groups are more connected throughout the novel and which are not. I find it particularly intriguing that upper-middle class families are all spread out along one giant social circle while the lower class families tend to cluster closer together as family groups.
Finally, I did one more thing with this graph. In the spirit of Franco Moretti’s work with Hamlet, where he graphed the social network of the novel and then deleted the Danish Prince from the graph, I did the same with both Gwendolen and Deronda, then told yEd to rearrange the groups based on the new data.
Okay, take a look at the two graphs.
I’d be mean and ask for your thoughts, but as I’m not sure how many of my readers have read Daniel Deronda (not to mention how many readers we have), it would be unfair to ask you for an interpretation. Instead, I will provide you with mine. So here’s the cool thing. The families that grouped together in the previous graph but not in this one were brought together by the actions of the main character–in this case, Deronda. So Mordecai rediscovered his long-lost sister Mirah through Deronda, for example. On the other hand, the families that now group together had their lives disrupted in the book by the actions of the main characters, either Deronda or Gwendolen, depending on the family in question. So if you look at Grandcourt, pictured here with his mistress, Mrs. Glasher and illegitimate heir, Henleigh, you’ll see that he’s nowhere near them in the graph with Gwendolen. In the text, Gwendolen marries Grandcourt despite knowing that he has a mistress and son who deserve to be legitimized. (Illegitimacy is a theme in this text.) I found it absolutely fascinating that removing the characters from the graph actually mimics what removing them from the book would have done.
So here’s my invitation to you: think about how else these graphs might be able to speak. I used them to construct a specific narrative of family ties throughout the novel based on how the connections behave. How else might you produce new elements of the novel’s narrative using these kinds of graphs? And, if you’ll think back to last week’s thoughts on dynamic social network graphs, how might those really help to structure questions about the novel?
One final note–I am really pleased to have finally produced something using statistical software that I think is pretty. It makes me feel that all is not yet lost.