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| IGO Network Analysis× | Alliance Network Analysis× | |
|---|---|---|
| Dziedzina | International Relations | International Relations |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 2004 | 2012 |
| Twórca≠ | Correlates of War IGO data (Pevehouse, Nordstrom & Warnke); network treatments by Ingram, Robinson & Busch and others | Skyler Cranmer, Bruce Desmarais & Elizabeth Menninga |
| Typ≠ | Affiliation/co-membership network analysis of international organizations | Network analysis and inferential network modeling of interstate alliances |
| Źródło pierwotne≠ | Pevehouse, J. C., Nordstrom, T., & Warnke, K. (2004). The Correlates of War 2 international governmental organizations data version 2.0. Conflict Management and Peace Science, 21(2), 101–119. DOI ↗ | Cranmer, S. J., Desmarais, B. A., & Menninga, E. J. (2012). Complex dependencies in the alliance network. Conflict Management and Peace Science, 29(3), 279–313. DOI ↗ |
| Inne nazwy | Intergovernmental Organization Network Analysis, IGO Co-membership Network, International Organization Network Analysis, IGO Affiliation Network | International Alliance Networks, Alliance Portfolio Network Analysis, Network Models of Alliance Formation, Interstate Alliance Graph Analysis |
| Pokrewne | 3 | 3 |
| Podsumowanie≠ | IGO network analysis treats the web of states' memberships in intergovernmental organizations as a network and studies its structure and consequences. Using membership data such as the Correlates of War IGO dataset (Pevehouse, Nordstrom, and Warnke 2004), it represents which states belong to which organizations as an affiliation network, projects it into a state-to-state graph of shared memberships, and analyzes how organizational ties bind states together — measuring integration into global governance, detecting institutional blocs, and testing whether shared IGO membership shapes outcomes like trade and conflict. | Alliance network analysis studies international alliances as a graph of states linked by formal security commitments, and models how that network forms and evolves. Rather than treating each alliance dyad as independent, it uses network science and inferential models such as the exponential random graph model (ERGM) — applied to alliance data by Cranmer, Desmarais, and Menninga (2012) — to capture the complex dependencies, such as a state's tendency to ally with its allies' allies, that ordinary dyadic regression assumes away. |
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