Quantifying the role of supernatural entities and the effect of missing data in Irish sagas

TL;DR

This paper uses complex network analysis on Irish sagas to identify central mythological characters, demonstrating robustness despite missing data, and offers a scalable approach for analyzing mythologies across cultures.

Contribution

It introduces a method to analyze mythological networks using a dictionary-based approach, bypassing extensive data collection and showing robustness to missing information.

Findings

Top characters identified by centrality measures align with known central figures.

Most central characters remain consistent despite large random edge removals.

Network analysis reveals robustness of key mythological figures in Irish sagas.

Abstract

For over a decade, complex networks have been applied to mythological texts in order to quantitatively compare them. This has allowed us to identify similarities between texts in different cultures, as well as to quantify the significance of some heroic characters. Analysing a full mythology of a culture requires gathering data from many individual myths which is time consuming and often impractical. In this work, we attempt to bypass this by analysing the network of characters in a dictionary of mythological characters. We show that the top characters identified by different centrality measures are consistent with central figures in the Irish sagas. Although much of Irish mythology has been lost, we demonstrate that these most central characters are highly robust to a large random removal of edges.