Least cost influence propagation in (social) networks

Matteo Fischetti, Michael Kahr, M. Leitner, Michele Monaci, Mario Ruthmair

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Influence maximization problems aim to identify key players in (social) networks and are typically motivated from viral marketing. In this work, we introduce and study the Generalized Least Cost Influence Problem (GLCIP) that generalizes many previously considered problem variants and allows to overcome some of their limitations. A formulation that is based on the concept of activation functions is proposed together with strengthening inequalities. Exact and heuristic solution methods are developed and compared for the new problem. Our computational results also show that our approaches outperform the state-of-the-art on relevant, special cases of the GLCIP.
Original languageEnglish
Pages (from-to)293-325
Number of pages33
JournalMathematical Programming
Volume170
Issue number1
DOIs
Publication statusPublished - 2018

Keywords

  • Influence Maximization
  • Mixed-Integer Programming
  • Social Network Analysis

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