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      Network Dynamics and the Evolution of International Cooperation

      American Political Science Review

      Cambridge University Press (CUP)

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          Abstract

          Cooperation helps states realize mutual gains, but mistrust and disagreements over institutional design inhibit cooperation. This article develops a network explanation for how states achieve cooperation in the face of persistent coordination and collaboration problems. The analysis focuses on bilateral cooperation agreements, a vast body of treaties spanning multiple issue areas. Bilateral agreements constitute an evolving network of cooperative ties. This network defines the strategic environment in which states bargain over new agreements, endogenously influencing subsequent bilateral endeavors by revealing strategically valuable information about states’ trustworthiness and preferences over institutional design, while also generating externalities that incentivize bilateral partnerships. Inferential network analysis shows that states are more likely to create bilateral agreements if they (1) share agreements with common third parties, (2) accede to more agreements in general, and/or (3) share important exogenous characteristics with current bilateral partners. These network dynamics drive bilateral cooperation in everything from commodities to cultural exchange to fisheries.

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          Most cited references 50

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          Emergence of scaling in random networks

          Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites. A model based on these two ingredients reproduces the observed stationary scale-free distributions, indicating that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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            The structure and function of complex networks

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            Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
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              An introduction to exponential random graph (p*) models for social networks

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                Author and article information

                Journal
                applab
                American Political Science Review
                Am Polit Sci Rev
                Cambridge University Press (CUP)
                0003-0554
                1537-5943
                November 2013
                October 2013
                : 107
                : 04
                : 766-785
                Article
                10.1017/S0003055413000440
                © 2013

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