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Populational Algorithm for Influence Maximization

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Computational Science and Its Applications -- ICCSA 2016 (ICCSA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9789))

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Abstract

Influence maximization is one of the most challenging tasks in network and consists in finding a set of the k seeder nodes which maximize the number of reached nodes, considering a propagation model. This work presents a Genetic Algorithm for influence maximization in networks considering Spreading Activation model for influence propagation. Four strategies for contructing the initial population were explored: a random strategy, a PageRank based strategy and two strategies which considers the community structure and the communities to which the seeders belong. The results show that GA was able to significantly improve the quality of the seeders, increasing the number of reached nodes in about \(25\,\%\).

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Acknowledgements

The authors thank the financial support agencies: Capes, FAPEMIG and CNPq.

Author information

Authors and Affiliations

  1. UFSJ/DCOMP - Federal University of São João del Rei, São João del Rei, MG, Brazil

    Carolina Ribeiro Xavier & Vinícius da Fonseca Vieira

  2. UFRJ/COPPE - Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil

    Alexandre Gonçalves Evsukoff

Authors
  1. Carolina Ribeiro Xavier
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  2. Vinícius da Fonseca Vieira
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  3. Alexandre Gonçalves Evsukoff
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Corresponding author

Correspondence to Carolina Ribeiro Xavier .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  5. Polytechnic University, Bari, Italy

    Carmelo M. Torre

  6. Monash University, Clayton, Victoria, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  9. Beijing Univ. of Posts & Telecomm., Beijing, China

    Shangguang Wang

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© 2016 Springer International Publishing Switzerland

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Xavier, C.R., da Fonseca Vieira, V., Evsukoff, A.G. (2016). Populational Algorithm for Influence Maximization. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9789. Springer, Cham. https://doi.org/10.1007/978-3-319-42089-9_25

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  • DOI: https://doi.org/10.1007/978-3-319-42089-9_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42088-2

  • Online ISBN: 978-3-319-42089-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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