Interconnected Kingdoms: Comparing 'A Song of Ice and Fire' Adaptations Across Media Using Complex Networks

TL;DR

Interconnected Kingdoms develops a rigorous cross-media framework to compare adaptations of the same narrative via dynamic character networks. It pairs graph-m-based character matching with narrative alignment, employing text-based, structure-based, and hybrid similarity measures across novels, comics, and the Game of Thrones TV show. Key findings show that character-interaction topology alone struggles to reliably map characters across media, but leveraging vertex attributes (e.g., affiliation) and neighborhood structure significantly boosts performance, especially for the most important characters; narrative alignment benefits from structure-based dynamic representations and from using commensurate narrative units, with hybrid methods offering the best results in many cases. The work provides a principled, data-driven approach to quantify cross-media divergence and offers a scalable blueprint for analyzing future adaptations across media types.

Abstract

In this article, we propose and apply a method to compare adaptations of the same story across different media. We tackle this task by modelling such adaptations through character networks. We compare them by leveraging two concepts at the core of storytelling: the characters involved, and the dynamics of the story. We propose several methods to match characters between media and compare their position in the networks; and perform narrative matching, i.e. match the sequences of narrative units that constitute the plots. We apply these methods to the novel series \textit{A Song of Ice and Fire}, by G.R.R. Martin, and its comics and TV show adaptations. Our results show that interactions between characters are not sufficient to properly match individual characters between adaptations, but that using some additional information such as character affiliation or gender significantly improves the performance. On the contrary, character interactions convey enough information to perform narrative matching, and allow us to detect the divergence between the original novels and its TV show adaptation.

Figures (20)

• Figure 1: The left panel shows the number of characters in plot time for each of the three media. On the right, the average degree $\langle k \rangle$ is displayed in time
• Figure 2: Similarity matrices obtained with Ružička's similarity, for the 20 most important characters of each pair of adaptations
• Figure 3: Spearman's correlation between the selected centrality metrics, for each of the three adaptations over period U2, considering the named (top row) and top-20 (bottom row) character set
• Figure 4: Centrality profiles of the common character set, over period U2, for all three adaptations. The five most important characters are represented in color
• Figure 5: Classes of common characters, detected based on their centrality profiles, over period U2, for all three adaptations. The five most important characters are represented in color