  The Influence Maximization problem under the Independent Cascade model (IC)
is considered. The problem asks for a minimal set of vertices to serve as "seed
set" from which a maximum influence propagation is expected. New seed-set
selection methods are introduced based on the notions of a $d$-packing and
vertex centrality. In particular, we focus on selecting seed-vertices that are
far apart and whose influence-values are the highest in their local
communities. Our best results are achieved via an initial computation of a
$d$-Packing followed by selecting either vertices of high degree or high
centrality in their respective closed neighborhoods. This overall "Pack and
Measure" approach proves highly effective as a seed selection method.
