On the role of network topology in German-Jewish recommendation letter networks in the early twentieth century
Gerlach L, Blumenthal DB (2023)
Publication Language: English
Publication Status: Published
Publication Type: Journal article, Original article
Publication year: 2023
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 8
Article Number: 24
Journal Issue: 1
DOI: 10.1007/s41109-023-00550-x
Open Access Link: https://doi.org/10.1007/s41109-023-00550-x
Abstract
Recommendation letters were an important instrument for orchestrating Jewish emigration from Germany in the late nineteenth and early twentieth century. Here, we present network-based analyses of manually collected meta-data from recommendation letters targeted at the Hebrew University (HU) in Jerusalem. Using standard semi-supervised node classification techniques and differential node centrality analyses, we show that the position of a recommendation letter in content-agnostic recommendation network models is predictive of its success, i.e., of whether or not the letter led to the recommendee obtaining a position at the HU. In particular, we show that authors of successful recommendation letters assume more central positions within the networks than authors of unsuccessful letters, while the opposite holds for the recommendation letters’ receivers. Beyond our application, these results showcase the potential of using network models for generating historical insights. Both the letter meta-data records and Python code to reproduce our analyses are available on GitHub: https://github.com/bionetslab/corrnet.
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Germany (DE)How to cite
APA:
Gerlach, L., & Blumenthal, D.B. (2023). On the role of network topology in German-Jewish recommendation letter networks in the early twentieth century. Applied Network Science, 8(1). https://doi.org/10.1007/s41109-023-00550-x
MLA:
Gerlach, Lisa, and David B. Blumenthal. "On the role of network topology in German-Jewish recommendation letter networks in the early twentieth century." Applied Network Science 8.1 (2023).
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