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Shadowing and shielding: Effective heuristics for continuous influence maximisation in the voting dynamics

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  • Guillermo Romero Moreno
  • Sukankana Chakraborty
  • Markus Brede

Abstract

Influence maximisation, or how to affect the intrinsic opinion dynamics of a social group, is relevant for many applications, such as information campaigns, political competition, or marketing. Previous literature on influence maximisation has mostly explored discrete allocations of influence, i.e. optimally choosing a finite fixed number of nodes to target. Here, we study the generalised problem of continuous influence maximisation where nodes can be targeted with flexible intensity. We focus on optimal influence allocations against a passive opponent and compare the structure of the solutions in the continuous and discrete regimes. We find that, whereas hub allocations play a central role in explaining optimal allocations in the discrete regime, their explanatory power is strongly reduced in the continuous regime. Instead, we find that optimal continuous strategies are very well described by two other patterns: (i) targeting the same nodes as the opponent (shadowing) and (ii) targeting direct neighbours of the opponent (shielding). Finally, we investigate the game-theoretic scenario of two active opponents and show that the unique pure Nash equilibrium is to target all nodes equally. These results expose fundamental differences in the solutions to discrete and continuous regimes and provide novel effective heuristics for continuous influence maximisation.

Suggested Citation

  • Guillermo Romero Moreno & Sukankana Chakraborty & Markus Brede, 2021. "Shadowing and shielding: Effective heuristics for continuous influence maximisation in the voting dynamics," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-21, June.
  • Handle: RePEc:plo:pone00:0252515
    DOI: 10.1371/journal.pone.0252515
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    Cited by:

    1. Kozitsin, Ivan V., 2024. "Optimal control in opinion dynamics models: diversity of influence mechanisms and complex influence hierarchies," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    2. Cai, Zhongqi & Gerding, Enrico & Brede, Markus, 2023. "Accelerating convergence of inference in the inverse Ising problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).

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