  We study the $r$-complex contagion influence maximization problem. In the
influence maximization problem, one chooses a fixed number of initial seeds in
a social network to maximize the spread of their influence. In the $r$-complex
contagion model, each uninfected vertex in the network becomes infected if it
has at least $r$ infected neighbors. In this paper, we focus on a random graph
model named the stochastic hierarchical blockmodel, which is a special case of
the well-studied stochastic blockmodel. When the graph is not exceptionally
sparse, in particular, when each edge appears with probability
$\omega(n^{-(1+1/r)})$, under certain mild assumptions, we prove that the
optimal seeding strategy is to put all the seeds in a single community. This
matches the intuition that in a nonsubmodular cascade model placing seeds near
each other creates synergy. However, it sharply contrasts with the intuition
for submodular cascade models (e.g., the independent cascade model and the
linear threshold model) in which nearby seeds tend to erode each others'
effects. Our key technique is a novel time-asynchronized coupling of four
cascade processes. Finally, we show that this observation yields a polynomial
time dynamic programming algorithm which outputs optimal seeds if each edge
appears with a probability either in $\omega(n^{-(1+1/r)})$ or in $o(n^{-2})$.
