# Authorship Attribution through Function Word Adjacency Networks

arXiv: https://arxiv.org/abs/1406.4469 · HTML: https://mlsanity.com/paper/1406.4469/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2014.

## Built on by (most cited first, 10 of 10)

- [Diversity-Enhanced Reasoning for Subjective Questions](https://mlsanity.com/paper/2507.20187/) (2025)
- [Node-Variant Graph Filters in Graph Neural Networks](https://mlsanity.com/paper/2106.00089/) (2021)
- [Text characterization based on recurrence networks](https://mlsanity.com/paper/2201.06665/) (2022)
- [Who could be behind QAnon? Authorship attribution with supervised machine-learning](https://mlsanity.com/paper/2303.02078/) (2023)
- [On the Trade-Off between Stability and Representational Capacity in Graph Neural Networks](https://mlsanity.com/paper/2312.02372/) (2023)
- [How Well Do LLMs Imitate Human Writing Style?](https://mlsanity.com/paper/2509.24930/) (2025)
- [Authorship identification under domain shift: a survey of stylistic measures and learned author representations](https://mlsanity.com/paper/2310.00436/) (2023)
- [Average shortest-path length in word-adjacency networks: Chinese versus English](https://mlsanity.com/paper/2601.06361/) (2026)
- [Non-Zipfian Distribution of Stopwords or Function Words and Subset Selection Models](https://mlsanity.com/paper/2603.04691/) (2026)
- [Creative Transformation in Literary Texts: Modelling Change Across Representational Levels](https://mlsanity.com/paper/2607.28513/) (2026)

## Abstract (arXiv)

A method for authorship attribution based on function word adjacency networks (WANs) is introduced. Function words are parts of speech that express grammatical relationships between other words but do not carry lexical meaning on their own. In the WANs in this paper, nodes are function words and directed edges stand in for the likelihood of finding the sink word in the ordered vicinity of the source word. WANs of different authors can be interpreted as transition probabilities of a Markov chain and are therefore compared in terms of their relative entropies. Optimal selection of WAN parameters is studied and attribution accuracy is benchmarked across a diverse pool of authors and varying text lengths. This analysis shows that, since function words are independent of content, their use tends to be specific to an author and that the relational data captured by function WANs is a good summary of stylometric fingerprints. Attribution accuracy is observed to exceed the one achieved by methods that rely on word frequencies alone. Further combining WANs with methods that rely on word frequencies alone, results in larger attribution accuracy, indicating that both sources of information encode different aspects of authorial styles.