  The identification of authorship in disputed documents still requires human
expertise, which is now unfeasible for many tasks owing to the large volumes of
text and authors in practical applications. In this study, we introduce a
methodology based on the dynamics of word co-occurrence networks representing
written texts to classify a corpus of 80 texts by 8 authors. The texts were
divided into sections with equal number of linguistic tokens, from which time
series were created for 12 topological metrics. The series were proven to be
stationary (p-value>0.05), which permits to use distribution moments as
learning attributes. With an optimized supervised learning procedure using a
Radial Basis Function Network, 68 out of 80 texts were correctly classified,
i.e. a remarkable 85% author matching success rate. Therefore, fluctuations in
purely dynamic network metrics were found to characterize authorship, thus
opening the way for the description of texts in terms of small evolving
networks. Moreover, the approach introduced allows for comparison of texts with
diverse characteristics in a simple, fast fashion.
