Pack and Measure: An Effective Approach for Influence Propagation in Social Networks
Social and Information Networks
2024-01-02 v1 Artificial Intelligence
Data Structures and Algorithms
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 -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 -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.
Keywords
Cite
@article{arxiv.2401.00525,
title = {Pack and Measure: An Effective Approach for Influence Propagation in Social Networks},
author = {Faisal N. Abu-Khzam and Ghinwa Bou Matar and Sergio Thoumi},
journal= {arXiv preprint arXiv:2401.00525},
year = {2024}
}