Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Trade Network Analysis× | Alliance Network Analysis× | |
|---|---|---|
| Domeniu | International Relations | International Relations |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 2013 | 2012 |
| Autorul original≠ | Network science applied to trade (e.g., Michael Ward, John Ahlquist & Arturas Rozenas) | Skyler Cranmer, Bruce Desmarais & Elizabeth Menninga |
| Tip≠ | Network and inferential-network analysis of trade flows | Network analysis and inferential network modeling of interstate alliances |
| Sursa seminală≠ | Ward, M. D., Ahlquist, J. S., & Rozenas, A. (2013). Gravity's rainbow: A dynamic latent space model for the world trade network. Network Science, 1(1), 95–118. 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 ↗ |
| Denumiri alternative | International Trade Network Analysis, World Trade Web Analysis, Trade Network Topology, Global Trade Graph Analysis | International Alliance Networks, Alliance Portfolio Network Analysis, Network Models of Alliance Formation, Interstate Alliance Graph Analysis |
| Înrudite | 3 | 3 |
| Rezumat≠ | Trade network analysis studies international trade as a weighted, directed graph in which states are nodes and trade flows are edges, then characterizes its structure and models how ties form. It moves beyond the standard dyadic gravity model by treating trade relationships as interdependent — a state's trade with one partner depends on the wider web of trade — and uses network science and inferential models such as latent space models (Ward, Ahlquist, and Rozenas 2013) to capture this dependence, identify hubs and blocs, and explain the architecture of the world trade system. | 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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