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.

翻译：本文研究了独立级联模型（IC）下的影响力最大化问题。该问题要求寻找一个最小规模的顶点集合作为“种子集”，期望从中实现最大化的影响力传播。基于$d$-包装和顶点中心性概念，我们引入了新的种子集选取方法。特别地，我们重点关注选取相互距离较远且在其局部社区中影响力值最高的种子顶点。我们的最佳成果通过以下方式实现：先计算一个$d$-包装，然后在其各自封闭邻域中选取高度数或高中心性的顶点。这种“包装与度量”整体方法被证明是一种高效的种子选取策略。