  Although influence maximization problem has been extensively studied over the
past ten years, majority of existing work adopt one of the following models:
\emph{full-feedback model} or \emph{zero-feedback model}. In the zero-feedback
model, we have to commit the seed users all at once in advance, this strategy
is also known as non-adaptive policy. In the full-feedback model, we select one
seed at a time and wait until the diffusion completes, before selecting the
next seed. Full-feedback model has better performance but potentially huge
delay, zero-feedback model has zero delay but poorer performance since it does
not utilize the observation that may be made during the seeding process. To
fill the gap between these two models, we propose \emph{Partial-feedback
Model}, which allows us to select a seed at any intermediate stage. We develop
two novel greedy policies that, for the first time, achieve bounded
approximation ratios under both uniform and non-uniform cost settings.
