# Authorship Attribution Based on Life-Like Network Automata

arXiv: https://arxiv.org/abs/1610.06498 · HTML: https://mlsanity.com/paper/1610.06498/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2016.

## Built on by (most cited first, 4 of 4)

- [Text characterization based on recurrence networks](https://mlsanity.com/paper/2201.06665/) (2022)
- [Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks](https://mlsanity.com/paper/2301.08364/) (2023)
- [Recovering network topology and dynamics via sequence characterization](https://mlsanity.com/paper/2206.15190/) (2022)
- [A Network Classification Method based on Density Time Evolution Patterns Extracted from Network Automata](https://mlsanity.com/paper/2211.13000/) (2022)

## Abstract (arXiv)

The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on word counts and related measurements have provided a simple, yet effective solution in particular cases; they are prone to manipulation. Recently, texts have been successfully modeled as networks, where words are represented by nodes linked according to textual similarity measurements. Such models are useful to identify informative topological patterns for the authorship recognition task. However, there is no consensus on which measurements should be used. Thus, we proposed a novel method to characterize text networks, by considering both topological and dynamical aspects of networks. Using concepts and methods from cellular automata theory, we devised a strategy to grasp informative spatio-temporal patterns from this model. Our experiments revealed an outperformance over traditional analysis relying only on topological measurements. Remarkably, we have found a dependence of pre-processing steps (such as the lemmatization) on the obtained results, a feature that has mostly been disregarded in related works. The optimized results obtained here pave the way for a better characterization of textual networks.