Identifying Key Players in the 1916 Rising using Centrality Measures and K-Shell Decomposition

Nagle, James (2023) Identifying Key Players in the 1916 Rising using Centrality Measures and K-Shell Decomposition. Masters thesis, Dublin, National College of Ireland.

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Abstract

Covert network analysis is largely dependent on a limited number of datasets created after a major event or the breakup of an organisation has occurred, leading to possible bias. Due to the lack of reliable data and their subjective nature it is often difficult to accurately evaluate the results of analysis on these networks. This research proposes a new network, based on intelligence reports compiled in the lead up to the 1916 Rising, on which covert network analysis can be conducted and which also contributes to a growing body of quantitative research on the Irish Revolutionary period. Several centrality measures commonly used for identifying influential nodes are calculated for each individual in the network to determine key players. K-shell decomposition is used to determine the individuals who inhabit the core of the network and community detection is used to identify closely connected factions. These results are then compared to the historical consensus, established over a century of qualitative analysis, to determine the suitability of each method. The analysis largely conforms to the established historical consensus and highlights the importance of figures whose role in the Rising is being re-evaluated in light of recently released documentation, indicating that the network can play a valuable role in assisting historical research and in serving as a benchmark for covert network analysis.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Hasanuzzaman, Mohammed UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science D History General and Old World > DA Great Britain > Ireland > Dublin
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	23 May 2023 15:28
Last Modified:	23 May 2023 15:28
URI:	https://norma.ncirl.ie/id/eprint/6624

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