# On the role of words in the network structure of texts: application to authorship attribution

arXiv: https://arxiv.org/abs/1705.04187 · HTML: https://mlsanity.com/paper/1705.04187/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2017.

## Built on by (most cited first, 3 of 3)

- [Quantitative Discourse Cohesion Analysis of Scientific Scholarly Texts using Multilayer Networks](https://mlsanity.com/paper/2205.07532/) (2022)
- [Using virtual edges to extract keywords from texts modeled as complex networks](https://mlsanity.com/paper/2205.02172/) (2022)
- [Recovering network topology and dynamics via sequence characterization](https://mlsanity.com/paper/2206.15190/) (2022)

## Abstract (arXiv)

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.