Temporal Network Analysis of Literary Texts

TL;DR

This paper applies temporal network analysis to literary texts, demonstrating that time-aware methods reveal insights into character importance and interactions that static analysis misses, using two contrasting works as case studies.

Contribution

It introduces the use of temporal network metrics to analyze literary character interactions, highlighting the advantages over traditional static approaches.

Findings

Temporal networks better capture story dynamics.

Time-averaged centralities reveal key characters.

Temporal analysis uncovers features static methods miss.

Abstract

We study temporal networks of characters in literature focusing on "Alice's Adventures in Wonderland" (1865) by Lewis Carroll and the anonymous "La Chanson de Roland" (around 1100). The former, one of the most influential pieces of nonsense literature ever written, describes the adventures of Alice in a fantasy world with logic plays interspersed along the narrative. The latter, a song of heroic deeds, depicts the Battle of Roncevaux in 778 A.D. during Charlemagne's campaign on the Iberian Peninsula. We apply methods recently developed by Taylor and coworkers \cite{Taylor+2015} to find time-averaged eigenvector centralities, Freeman indices and vitalities of characters. We show that temporal networks are more appropriate than static ones for studying stories, as they capture features that the time-independent approaches fail to yield.