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Influence Maximization and Extremal Optimization

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Hybrid Artificial Intelligent Systems (HAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11734))

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Abstract

Influence Extremal Optimization (InfEO) is an algorithm based on Extremal Optimization, adapted for the influence maximization problem for the independent cascade model. InfEO maximizes the marginal contribution of a node to the influence set of the model. Numerical experiments are used to compare InfEO with other influence maximization methods, indicating the potential of this approach. Practical results are discussed on a network constructed from publication data in the field of computer science.

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Notes

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Acknowledgement

This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number PN-III-P1-1.1-TE-2016-1933.

Author information

Authors and Affiliations

  1. Babeş-Bolyai University, Cluj-Napoca, Romania

    Tamás Képes, Noémi Gaskó, Rodica Ioana Lung & Mihai-Alexandru Suciu

Authors
  1. Tamás Képes
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  2. Noémi Gaskó
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  3. Rodica Ioana Lung
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  4. Mihai-Alexandru Suciu
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Corresponding author

Correspondence to Noémi Gaskó .

Editor information

Editors and Affiliations

  1. University of León, León, Spain

    Hilde Pérez García

  2. University of León, León, Spain

    Lidia Sánchez González

  3. University of León, León, Spain

    Manuel Castejón Limas

  4. University of A Coruña, Ferrol, Spain

    Héctor Quintián Pardo

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado Rodríguez

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Képes, T., Gaskó, N., Lung, R.I., Suciu, MA. (2019). Influence Maximization and Extremal Optimization. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_36

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  • DOI: https://doi.org/10.1007/978-3-030-29859-3_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29858-6

  • Online ISBN: 978-3-030-29859-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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