Concentric network symmetry grasps authors' styles in word adjacency networks

TL;DR

This paper explores the use of concentric network symmetry in word adjacency networks to analyze textual styles, revealing that symmetry patterns can distinguish authors and improve authorship attribution accuracy.

Contribution

It introduces a novel symmetry-based network analysis method for texts, demonstrating its effectiveness in authorship attribution and its independence from traditional network metrics.

Findings

Symmetry distributions follow a power-law pattern in novels.

Symmetry metrics do not correlate with degree or betweenness centrality.

Authors exhibit unique symmetric motif preferences, enabling 82.5% accuracy in authorship identification.

Abstract

Several characteristics of written texts have been inferred from statistical analysis derived from networked models. Even though many network measurements have been adapted to study textual properties at several levels of complexity, some textual aspects have been disregarded. In this paper, we study the symmetry of word adjacency networks, a well-known representation of text as a graph. A statistical analysis of the symmetry distribution performed in several novels showed that most of the words do not display symmetric patterns of connectivity. More specifically, the merged symmetry displayed a distribution similar to the ubiquitous power-law distribution. Our experiments also revealed that the studied metrics do not correlate with other traditional network measurements, such as the degree or betweenness centrality. The effectiveness of the symmetry measurements was verified in the authorship attribution task. Interestingly, we found that specific authors prefer particular types of symmetric motifs. As a consequence, the authorship of books could be accurately identified in 82.5% of the cases, in a dataset comprising books written by 8 authors. Because the proposed measurements for text analysis are complementary to the traditional approach, they can be used to improve the characterization of text networks, which might be useful for related applications, such as those relying on the identification of topical words and information retrieval.