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

TL;DR

This work tackles Influence Maximization under the Independent Cascade model by introducing a Pack and Measure strategy: first compute a $d$-packing to force seed diversity, then apply centrality-based or diminishing-influence selection within each packing element's neighborhood. The core contributions are (i) a $d$-packing framework that reduces seed neighborhood overlap, (ii) the Diminishing Influence heuristic that weights local reach with a decaying radius, and (iii) a speed-of-propagation metric to compare seed sets. Empirical results on SNAP networks and synthetic tests show improved network coverage and faster diffusion, especially in networks with scattered dense communities. The approach offers a practical, tunable seed-selection pipeline with potential extensions to dynamic networks and related diffusion problems.

Abstract

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.