  A widely studied model for influence diffusion in social networks are {\it
target sets}. For a graph $G$ and an integer-valued threshold function $\tau$
on its vertex set, a {\it target set} or {\it dynamic monopoly} is a set of
vertices of $G$ such that iteratively adding to it vertices $u$ of $G$ that
have at least $\tau(u)$ neighbors in it eventually yields the entire vertex set
of $G$. This notion is limited to the binary choice of including a vertex in
the target set or not, and Cordasco et al.~proposed {\it partial incentives} as
a variant allowing for intermediate choices.
  We show that finding optimal partial incentives is hard for chordal graphs
and planar graphs but tractable for graphs of bounded treewidth and for
interval graphs with bounded thresholds. We also contribute some new results
about target set seletion on planar graphs by showing the hardness of this
problem, and by describing an efficient $O(\sqrt{n})$-approximation algorithm
as well as a PTAS for the dual problem of finding a maximum degenerate set.
