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

arXiv: https://arxiv.org/abs/2401.00525 · HTML: https://mlsanity.com/paper/2401.00525/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 31 Dec 2023.

## Builds on (most cited first, 1 of 1)

- [Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency](https://mlsanity.com/paper/1404.0900/) (2014)

## Abstract (arXiv)

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.