  In this paper, we explore a set of novel features for authorship attribution
of documents. These features are derived from a word network representation of
natural language text. As has been noted in previous studies, natural language
tends to show complex network structure at word level, with low degrees of
separation and scale-free (power law) degree distribution. There has also been
work on authorship attribution that incorporates ideas from complex networks.
The goal of our paper is to explore properties of these complex networks that
are suitable as features for machine-learning-based authorship attribution of
documents. We performed experiments on three different datasets, and obtained
promising results.
