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Mathematical Programming

, Volume 170, Issue 1, pp 293–325 | Cite as

Least cost influence propagation in (social) networks

  • Matteo Fischetti
  • Michael Kahr
  • Markus Leitner
  • Michele Monaci
  • Mario Ruthmair
Full Length Paper Series B
  • 283 Downloads

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.

Keywords

Influence maximization Mixed-integer programming Social network analysis 

Mathematics Subject Classification

90B10 90C11 90C27 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society 2018

Authors and Affiliations

  1. 1.DEIUniversity of PaduaPaduaItaly
  2. 2.Department of Statistics and Operations ResearchUniversity of ViennaViennaAustria
  3. 3.DEIUniversity of BolognaBolognaItaly

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