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Large-scale influence maximization via maximal covering location

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

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.

Suggested Citation

  • Güney, Evren & Leitner, Markus & Ruthmair, Mario & Sinnl, Markus, 2021. "Large-scale influence maximization via maximal covering location," European Journal of Operational Research, Elsevier, vol. 289(1), pages 144-164.
    • Handle: RePEc:eee:ejores:v:289:y:2021:i:1:p:144-164
    DOI: 10.1016/j.ejor.2020.06.028
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    1. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions - 1," LIDAM Reprints CORE 334, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Vatsa, Amit Kumar & Jayaswal, Sachin, 2016. "A new formulation and Benders decomposition for the multi-period maximal covering facility location problem with server uncertainty," European Journal of Operational Research, Elsevier, vol. 251(2), pages 404-418.
    3. Hao-Hsiang Wu & Simge Küçükyavuz, 2018. "A two-stage stochastic programming approach for influence maximization in social networks," Computational Optimization and Applications, Springer, vol. 69(3), pages 563-595, April.
    4. Pietro Panzarasa & Tore Opsahl & Kathleen M. Carley, 2009. "Patterns and dynamics of users' behavior and interaction: Network analysis of an online community," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(5), pages 911-932, May.
    5. Cordeau, Jean-François & Furini, Fabio & Ljubić, Ivana, 2019. "Benders decomposition for very large scale partial set covering and maximal covering location problems," European Journal of Operational Research, Elsevier, vol. 275(3), pages 882-896.
    6. David L Gibbs & Ilya Shmulevich, 2017. "Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-19, June.
    7. Richard Church & Charles R. Velle, 1974. "The Maximal Covering Location Problem," Papers in Regional Science, Wiley Blackwell, vol. 32(1), pages 101-118, January.
    8. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions," LIDAM Reprints CORE 341, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Rahmaniani, Ragheb & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2017. "The Benders decomposition algorithm: A literature review," European Journal of Operational Research, Elsevier, vol. 259(3), pages 801-817.
    10. Nemhauser, G.L. & Wolsey, L.A., 1981. "Maximizing submodular set functions: formulations and analysis of algorithms," LIDAM Reprints CORE 455, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    11. Fischetti, Matteo & Ljubić, Ivana & Sinnl, Markus, 2016. "Benders decomposition without separability: A computational study for capacitated facility location problems," European Journal of Operational Research, Elsevier, vol. 253(3), pages 557-569.
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    Cited by:

    1. Eszter Julianna Csókás & Tamás Vinkó, 2023. "An exact method for influence maximization based on deterministic linear threshold model," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(1), pages 269-286, March.
    2. S. Raghavan & Rui Zhang, 2022. "Rapid Influence Maximization on Social Networks: The Positive Influence Dominating Set Problem," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1345-1365, May.
    3. Chen, Liang & Chen, Sheng-Jie & Chen, Wei-Kun & Dai, Yu-Hong & Quan, Tao & Chen, Juan, 2023. "Efficient presolving methods for solving maximal covering and partial set covering location problems," European Journal of Operational Research, Elsevier, vol. 311(1), pages 73-87.

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