Can historians trust centrality?: Historical network analysis and centrality metrics robustness
In this paper, we consider four measures of centrality (betweenness, closeness, degree and eigenvalue centrality) in their use in the analysis of historical networks. Since the sources used by historians to construct such networks are by nature incomplete and imperfect, it is necessary to take into...
| Main Author: | |
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| Format: | Electronic Article |
| Language: | English |
| Check availability: | HBZ Gateway |
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| Fernleihe: | Fernleihe für die Fachinformationsdienste |
| Published: |
Université du Luxembourg
2021
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| In: |
Journal of historical network research
Year: 2021, Volume: 6, Issue: 1, Pages: 85-125 |
| Further subjects: | B
Methodology
B Centrality B Robustness |
| Online Access: |
Volltext (kostenfrei) Volltext (kostenfrei) |
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| 520 | |a In this paper, we consider four measures of centrality (betweenness, closeness, degree and eigenvalue centrality) in their use in the analysis of historical networks. Since the sources used by historians to construct such networks are by nature incomplete and imperfect, it is necessary to take into account as much as possible the robustness of these metrics, i.e., their stability with respect to the hazards that time has inflicted on historical documents. To study this, we apply a battery of tests to three networks constructed from medieval history data. The first one is a political history network, which represents the links between protagonists of the conflict for the episcopal see of Cambrai in the 11th century. The second one is a network of socio-economic history, describing the credit relations of merchants in Ypres in the 13th century. The third one is a hagiographic network that depicts the connections between lives of saints often compiled together in manuscripts. These tests are designed to simulate the processes of disappearance and degradation of the information contained in sources by imitating as closely as possible the situations historians face when manipulating graphs. In each of them, we create a large set of new graphs by transforming the original graphs, and observe the effect of these transformations on the centrality metrics. For this, we use a random process, but which respects the particularities of the considered networks, which are built from historical sources. Our results allow us to assess the general relevance of the use of centrality in historical network analysis, to compare the four metrics studied in terms of robustness, and to identify a set of methodological points to which the historian applying such techniques must pay particular attention. | ||
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