  We investigate social networks of characters found in cultural works such as
novels and films. These character networks exhibit many of the properties of
complex networks such as skewed degree distribution and community structure,
but may be of relatively small order with a high multiplicity of edges.
Building on recent work of beveridge, we consider graph extraction,
visualization, and network statistics for three novels: Twilight by Stephanie
Meyer, Steven King's The Stand, and J.K. Rowling's Harry Potter and the Goblet
of Fire. Coupling with 800 character networks from films found in the
http://moviegalaxies.com/ database, we compare the data sets to simulations
from various stochastic complex networks models including random graphs with
given expected degrees (also known as the Chung-Lu model), the configuration
model, and the preferential attachment model. Using machine learning techniques
based on motif (or small subgraph) counts, we determine that the Chung-Lu model
best fits character networks and we conjecture why this may be the case.
