  We investigate how well traditional fiction genres like Fantasy, Thriller,
and Literature represent readers' preferences. Using user data from Goodreads
we construct a book network where two books are strongly linked if the same
people tend to read or enjoy them both. We then partition this network into
communities of similar books and assign each a list of subjects from The Open
Library to serve as a proxy for traditional genres. Our analysis reveals that
the network communities correspond to existing combinations of traditional
genres, but that the exact communities differ depending on whether we consider
books that people read or books that people enjoy.
  In addition, we apply principal component analysis to the data and find that
the variance in the book communities is best explained by two factors: the
maturity/childishness and realism/fantastical nature of the books. We propose
using this maturity-realism plane as a coarse classification tool for stories.
