# Linguistic data mining with complex networks: a stylometric-oriented approach

arXiv: https://arxiv.org/abs/1808.05439 · HTML: https://mlsanity.com/paper/1808.05439/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2018.

## Built on by (most cited first, 8 of 8)

- [Complex systems approach to natural language](https://mlsanity.com/paper/2401.02772/) (2024)
- [Universal versus system-specific features of punctuation usage patterns in~major Western~languages](https://mlsanity.com/paper/2212.11182/) (2022)
- [Text characterization based on recurrence networks](https://mlsanity.com/paper/2201.06665/) (2022)
- [Statistics of punctuation in experimental literature -- the remarkable case of "Finnegans Wake" by James Joyce](https://mlsanity.com/paper/2409.00483/) (2024)
- [Punctuation patterns in "Finnegans Wake" by James Joyce are largely translation-invariant](https://mlsanity.com/paper/2501.12954/) (2025)
- [Multifractal hopscotch in "Hopscotch" by Julio Cortazar](https://mlsanity.com/paper/2501.12955/) (2025)
- [A ripple in time: a discontinuity in American history](https://mlsanity.com/paper/2312.01185/) (2023)
- [Average shortest-path length in word-adjacency networks: Chinese versus English](https://mlsanity.com/paper/2601.06361/) (2026)

## Abstract (arXiv)

By representing a text by a set of words and their co-occurrences, one obtains a word-adjacency network being a reduced representation of a given language sample. In this paper, the possibility of using network representation to extract information about individual language styles of literary texts is studied. By determining selected quantitative characteristics of the networks and applying machine learning algorithms, it is possible to distinguish between texts of different authors. Within the studied set of texts, English and Polish, a properly rescaled weighted clustering coefficients and weighted degrees of only a few nodes in the word-adjacency networks are sufficient to obtain the authorship attribution accuracy over 90%. A correspondence between the text authorship and the word-adjacency network structure can therefore be found. The network representation allows to distinguish individual language styles by comparing the way the authors use particular words and punctuation marks. The presented approach can be viewed as a generalization of the authorship attribution methods based on simple lexical features. Additionally, other network parameters are studied, both local and global ones, for both the unweighted and weighted networks. Their potential to capture the writing style diversity is discussed; some differences between languages are observed.