A pledged community? Using community detection to analyze autocratic cooperation in UN co-sponsorship networks


Meyer, Cosima ; Hammerschmidt, Dennis



DOI: https://doi.org/10.1007/978-3-030-65347-7_15
URL: https://link.springer.com/chapter/10.1007/978-3-03...
Document Type: Conference or workshop publication
Year of publication: 2021
Book title: Complex Networks & Their Applications IX : Volume 1, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020
The title of a journal, publication series: Studies in Computational Intelligence
Volume: 943
Page range: 177-188
Conference title: COMPLEX NETWORKS 2020
Location of the conference venue: Online
Date of the conference: 01.-03.12.2020
Publisher: Benito, Rosa M. ; Cherifi, Chatal ; Cherifi, Hocine ; Moro, Esteban ; Rocha, Luis Mateus ; Sales-Pardo, Marta
Place of publication: Cham
Publishing house: Springer International Publishing
ISBN: 978-3-030-65346-0 , 978-3-030-65348-4 , 978-3-030-65349-1 , 978-3-030-65347-7
Publication language: English
Institution: Außerfakultäre Einrichtungen > Mannheim Centre for European Social Research - Research Department A
Subject: 300 Social sciences, sociology, anthropology
Abstract: Autocratic cooperation is difficult to study. Democratic states usually disfavor autocratic cooperation partners because they are perceived as less reliable and do not sign agreements with them. While it is challenging to capture autocratic cooperation with traditional approaches such as signed alliance treaties, co-sponsorship at the United Nations General Assembly (UNGA) offers a valuable alternative. UNGA co-sponsorship is less binding than alliances, allowing states to cooperate more freely with one another. What is more, states are required to choose cooperation partners actively. This allows us to study how autocracies cooperate in the international system at a venue that overcomes common restrictions to autocratic cooperation. We construct co-sponsorship networks at the UNGA and use the Leiden algorithm to identify community clusters. Our multiclass random forest classification model supports our assumption and shows that regime type is associated with cooperation clusters in UNGA co-sponsorship networks.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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