  The vast amount of data and increase of computational capacity have allowed
the analysis of texts from several perspectives, including the representation
of texts as complex networks. Nodes of the network represent the words, and
edges represent some relationship, usually word co-occurrence. Even though
networked representations have been applied to study some tasks, such
approaches are not usually combined with traditional models relying upon
statistical paradigms. Because networked models are able to grasp textual
patterns, we devised a hybrid classifier, called labelled subgraphs, that
combines the frequency of common words with small structures found in the
topology of the network, known as motifs. Our approach is illustrated in two
contexts, authorship attribution and translationese identification. In the
former, a set of novels written by different authors is analyzed. To identify
translationese, texts from the Canadian Hansard and the European parliament
were classified as to original and translated instances. Our results suggest
that labelled subgraphs are able to represent texts and it should be further
explored in other tasks, such as the analysis of text complexity, language
proficiency, and machine translation.
