Large-scale influence maximization via maximal covering location

Evren Güney, Markus Leitner, Mario Ruthmair*, Markus Sinnl

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Influence maximization aims at identifying a limited set of key individuals in a (social) network which spreads information based on some propagation model and maximizes the number of individuals reached. We show that influence maximization based on the probabilistic independent cascade model can be modeled as a stochastic maximal covering location problem. A reformulation based on Benders decomposition is proposed and a relation between obtained Benders optimality cuts and submodular cuts for correspondingly defined subsets is established. We introduce preprocessing tests, which allow us to remove variables from the model and develop efficient algorithms for the separation of Benders cuts. Both aspects are shown to be crucial ingredients of the developed branch-and-cut algorithm since real-life social network instances may be very large. In a computational study, the considered variants of this branch-and-cut algorithm outperform the state-of-the-art approach for influence maximization by orders of magnitude.

Original languageEnglish
Pages (from-to)144-164
Number of pages21
JournalEuropean Journal of Operational Research
Volume289
Issue number1
DOIs
Publication statusPublished - 16 Feb 2021

Keywords

  • Influence maximization
  • Integer programming
  • Large scale optimization
  • Networks
  • Stochastic programming

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