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Multi-objective Evolutionary Algorithms for Influence Maximization in Social Networks

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Applications of Evolutionary Computation (EvoApplications 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10199))

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Abstract

As the pervasiveness of social networks increases, new NP-hard related problems become interesting for the optimization community. The objective of influence maximization is to contact the largest possible number of nodes in a network, starting from a small set of seed nodes, and assuming a model for information propagation. This problem is of utmost practical importance for applications ranging from social studies to marketing. The influence maximization problem is typically formulated assuming that the number of the seed nodes is a parameter. Differently, in this paper, we choose to formulate it in a multi-objective fashion, considering the minimization of the number of seed nodes among the goals, and we tackle it with an evolutionary approach. As a result, we are able to identify sets of seed nodes of different size that spread influence the best, providing factual data to trade-off costs with quality of the result. The methodology is tested on two real-world case studies, using two different influence propagation models, and compared against state-of-the-art heuristic algorithms. The results show that the proposed approach is almost always able to outperform the heuristics.

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Notes

  1. 1.

    http://ugp3.sourceforge.net/.

  2. 2.

    https://arxiv.org/.

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Acknowledgment

Andrea Marcelli Ph.D. program at Politecnico di Torino is supported by a fellowship from TIM (Telecom Italia Group).

This article is based upon work from COST Action CA15140 ‘Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO)’ supported by COST (European Cooperation in Science and Technology).

Author information

Authors and Affiliations

  1. University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands

    Doina Bucur

  2. INCAS, Dr. Nassaulaan 9, 9401 HJ, Assen, The Netherlands

    Giovanni Iacca

  3. DAUIN, Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129, Torino, Italy

    Andrea Marcelli & Giovanni Squillero

  4. INRA, UMR 782 GMPA, Avenue Lucien Brétignières, 78850, Thiverval-Grignon, France

    Alberto Tonda

Authors
  1. Doina Bucur
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  2. Giovanni Iacca
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  3. Andrea Marcelli
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  4. Giovanni Squillero
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  5. Alberto Tonda
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Corresponding author

Correspondence to Andrea Marcelli .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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Bucur, D., Iacca, G., Marcelli, A., Squillero, G., Tonda, A. (2017). Multi-objective Evolutionary Algorithms for Influence Maximization in Social Networks. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_15

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  • DOI: https://doi.org/10.1007/978-3-319-55849-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55848-6

  • Online ISBN: 978-3-319-55849-3

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