  By representing a text by a set of words and their co-occurrences, one
obtains a word-adjacency network being a reduced representation of a given
language sample. In this paper, the possibility of using network representation
to extract information about individual language styles of literary texts is
studied. By determining selected quantitative characteristics of the networks
and applying machine learning algorithms, it is possible to distinguish between
texts of different authors. Within the studied set of texts, English and
Polish, a properly rescaled weighted clustering coefficients and weighted
degrees of only a few nodes in the word-adjacency networks are sufficient to
obtain the authorship attribution accuracy over 90%. A correspondence between
the text authorship and the word-adjacency network structure can therefore be
found. The network representation allows to distinguish individual language
styles by comparing the way the authors use particular words and punctuation
marks. The presented approach can be viewed as a generalization of the
authorship attribution methods based on simple lexical features.
  Additionally, other network parameters are studied, both local and global
ones, for both the unweighted and weighted networks. Their potential to capture
the writing style diversity is discussed; some differences between languages
are observed.
