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Large-Scale Multi-objective Influence Maximisation with Network Downscaling

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13399)

Abstract

Finding the most influential nodes in a network is a computationally hard problem with several possible applications in various kinds of network-based problems. While several methods have been proposed for tackling the influence maximisation (IM) problem, their runtime typically scales poorly when the network size increases. Here, we propose an original method, based on network downscaling, that allows a multi-objective evolutionary algorithm (MOEA) to solve the IM problem on a reduced scale network, while preserving the relevant properties of the original network. The downscaled solution is then upscaled to the original network, using a mechanism based on centrality metrics such as PageRank. Our results on eight large networks (including two with \(\sim \)50k nodes) demonstrate the effectiveness of the proposed method with a more than 10-fold runtime gain compared to the time needed on the original network, and an up to \(82\%\) time reduction compared to CELF.

Keywords

  • Social network
  • Influence maximisation
  • Complex network
  • Genetic algorithm
  • Multi-objective optimisation

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Notes

  1. 1.

    Code available at: https://github.com/eliacunegatti/Influence-Maximization.

  2. 2.

    https://graph-tool.skewed.de.

  3. 3.

    We also compared directly the HV values. We applied the Wilcoxon Rank-Sum test (with \(\alpha =0.05\)) to analyse whether the HV values calculated on the downscaled solutions and the upscaled ones (with upscaling based on PageRank) were significantly different from the HV values obtained on the original network, all on 10 runs. All the pairwise comparisons resulted statistically significant, excluding the one related to the downscaled solutions found on Fb. Pag. with \(s=2\) and WC model.

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Correspondence to Giovanni Iacca .

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Cunegatti, E., Iacca, G., Bucur, D. (2022). Large-Scale Multi-objective Influence Maximisation with Network Downscaling. In: Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tušar, T. (eds) Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022. Lecture Notes in Computer Science, vol 13399. Springer, Cham. https://doi.org/10.1007/978-3-031-14721-0_15

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  • DOI: https://doi.org/10.1007/978-3-031-14721-0_15

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