  Well-established automatic analyses of texts mainly consider frequencies of
linguistic units, e.g. letters, words and bigrams, while methods based on
co-occurrence networks consider the structure of texts regardless of the nodes
label (i.e. the words semantics). In this paper, we reconcile these distinct
viewpoints by introducing a generalized similarity measure to compare texts
which accounts for both the network structure of texts and the role of
individual words in the networks. We use the similarity measure for authorship
attribution of three collections of books, each composed of 8 authors and 10
books per author. High accuracy rates were obtained with typical values from
90% to 98.75%, much higher than with the traditional the TF-IDF approach for
the same collections. These accuracies are also higher than taking only the
topology of networks into account. We conclude that the different properties of
specific words on the macroscopic scale structure of a whole text are as
relevant as their frequency of appearance; conversely, considering the identity
of nodes brings further knowledge about a piece of text represented as a
network.
