Network games provide a basic framework for studying the diffusion of new ideas
or behaviors through a population. In these models, agents decide to adopt a
new
idea based on optimizing pay-off that depends on the adoption decisions of
their
neighbors in an underlying network. Assuming such a model, we consider the
problem of inferring early adopters or first movers given a snap shot of the
adoption state at a given time. In particular we focus on reducing the
complexity of such inference problems for large networks.