  Measuring influence and determining what drives it are persistent questions
in political science and in network analysis more generally. Herein we focus on
the domain of international relations. Our major substantive question is: How
can we determine what characteristics make an actor influential? To address the
topic of influence, we build on a multilinear tensor regression framework
(MLTR) that captures influence relationships using a tensor generalization of a
vector autoregression model. Influence relationships in that approach are
captured in a pair of n x n matrices and provide measurements of how the
network actions of one actor may influence the future actions of another. A
limitation of the MLTR and earlier latent space approaches is that there are no
direct mechanisms through which to explain why a certain actor is more or less
influential than others. Our new framework, social influence regression,
provides a way to statistically model the influence of one actor on another as
a function of characteristics of the actors. Thus we can move beyond just
estimating that an actor influences another to understanding why. To highlight
the utility of this approach, we apply it to studying monthly-level conflictual
events between countries as measured through the Integrated Crisis Early
Warning System (ICEWS) event data project.
