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Transmission errors and influence maximization in the voter model

Transmission errors and influence maximization in the voter model
Transmission errors and influence maximization in the voter model
In this paper we analyze the effects of mistakes in opinion propagation in the voter model on strategic influence maximization. We provide numerical results and analytical arguments to show that generally two regimes exist for optimal opinion control: a regime of low transmission errors in which influence maximizers should focus on hub nodes and a large-error regime in which influence maximizers should focus on low-degree nodes. We also develop a degree-based mean-field theory and apply it to random networks with bimodal degree distribution, finding that analytical results for the dependence of regimes on parameters qualitatively agree with numerical results for scale-free networks. We generally find that the regime of optimal hub control is the larger, the more heterogeneous the social network and the smaller the more resources both available to the influencers.
1742-5468
1-15
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Restocchi, Valerio
39654f4e-2a84-4c78-a853-081704568415
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Restocchi, Valerio
39654f4e-2a84-4c78-a853-081704568415
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b

Brede, Markus, Restocchi, Valerio and Stein, Sebastian (2019) Transmission errors and influence maximization in the voter model. Journal of Statistical Mechanics: Theory and Experiment, 1-15. (doi:10.1088/1742-5468/ab054b).

Record type: Article

Abstract

In this paper we analyze the effects of mistakes in opinion propagation in the voter model on strategic influence maximization. We provide numerical results and analytical arguments to show that generally two regimes exist for optimal opinion control: a regime of low transmission errors in which influence maximizers should focus on hub nodes and a large-error regime in which influence maximizers should focus on low-degree nodes. We also develop a degree-based mean-field theory and apply it to random networks with bimodal degree distribution, finding that analytical results for the dependence of regimes on parameters qualitatively agree with numerical results for scale-free networks. We generally find that the regime of optimal hub control is the larger, the more heterogeneous the social network and the smaller the more resources both available to the influencers.

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Accepted/In Press date: 31 January 2019
e-pub ahead of print date: 15 March 2019

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Local EPrints ID: 428103
URI: http://eprints.soton.ac.uk/id/eprint/428103
ISSN: 1742-5468
PURE UUID: c248b95c-1adc-4ae4-91b7-9a7263984b72

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Date deposited: 11 Feb 2019 17:30
Last modified: 07 Oct 2020 06:30

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Contributors

Author: Markus Brede
Author: Valerio Restocchi
Author: Sebastian Stein

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