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

Pack and Measure: An Effective Approach for Influence Propagation in Social Networks