This is the first post of a series on network visualisation.
Thanks to the facilitated access to network analysis tools and the growing interest in many disciplines towards studying the relations structuring datasets, networks have become ubiquitous objects in science, in newspapers, on tech book covers, all over the Web, and to illustrate anything big data-related (hand in hand with word clouds.). Unfortunately, the resort to networks has reached a point where in a conference I heard a speaker say:
“Since this is mandatory, here is a network visualisation of these data. Sorry if you cannot see anything in this big hairball.“
You would expect in a conference that everything presented has a purpose. Sadly, it seems that there is underlying pressure in scientific communities to create such horrors.
In this work, we investigate how a selection of centrality measures can be used to differentiate roles of characters in Jean-Jacques Rousseau’s autobiography Les Confessions. We define methods to build automatically a network of characters, based on their co-occurrences. In the resulting network, each character of the novel is a node connected to other nodes representing other characters. We rank these with three centrality measures and find different ordering depending on the measures. We highlight how characters with high betweenness centrality tend to play positive roles in the narration as they act as important mediators and facilitators of Rousseau’s social life. On the contrary, we show that characters with high eigenvector centrality form a cluster of interchangeable figures, acting in practice like a “meta-character”, a crowd that conspires against Rousseau. Although we cannot yet generalise these findings to other work, we argue that these preliminary results motivate further research based on well-chosen centrality measures in digital literary studies. Continue reading “Centrality measures as a signature of roles in Rousseau’s Les Confessions”