Authorship attribution using function words adjacency networks

Santiago Segarra, Mark Eisen, Alejandro Ribeiro

Abstract: We present an authorship attribution method based on relational data between function words. These are content independent words that help define grammatical relationships. As relational structures we use normalized word adjacency networks. We interpret these networks as Markov chains and compare them using entropy measures. We illustrate the accuracy of the method developed through a series of numerical experiments including comparisons with frequency based methods. We show that accuracy increases when combining relational and frequency based data, indicating that both sources of information encode different aspects of authorial styles.