ERGM results for assessing influence network structure.

Models reflect the best fitting ERGM found for each network, diagnostics indicate convergence of Markov chains. Geometric weights (GW) of 0.1, reflecting a moderate degree of down-weighting, were used for all GW terms including Geometrically Weighted In-Degree (GW In-Degree) or Out-Degree (GW Out-Degree). ERGM model results illustrate the structural features of each network. Negative State Influence: This network is constituted by relatively few negative influencees (negative and robust effect for GW In-Degree) and constituted by relatively few instances of being induced to inaction by democracies (negative and robust effect for Sender Regime Score). Positive State Influence: This network is marked by states influencing others of similar capabilities (negative and robust effect for dyadic GDP difference variable). In addition, those who are positively influenced are influenced by few states (negative and robust effect for GW In-Degree). Alternatively, those that influence others, are unlikely to influence many (negative and robust effect for GW Out-Degree Term) Negative Treaty Influence: Network has no real discernible features. Positive Treaty Influence: Network has no real discernible features. Should treaties positively influence other treaties, they are likely to influence few treaties (negative and robust effect for GW Out-Degree term). Should treaties be positively influenced by other treaties, they are likely to be influenced by few treaties (negative and robust effect for GW In-Degree).