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Effects of time horizons on Influence maximization in the voter dynamics

Effects of time horizons on Influence maximization in the voter dynamics
Effects of time horizons on Influence maximization in the voter dynamics
In this paper we analyze influence maximization in the voter model with an active strategic and a passive influencing party in non-stationary settings. We thus explore the dependence of optimal influence allocation on the time horizons of the strategic influencer. We find that on undirected heterogeneous networks, for short time horizons, influence is maximized when targeting low degree nodes, while for long time horizons influence maximization is achieved when controlling hub nodes. Furthermore, we show that for short and intermediate time scales influence maximization can exploit knowledge of (transient) opinion configurations. More in detail, we find two rules. First, nodes with states differing from the strategic influencer’s goal should be targeted. Second, if only few nodes are initially aligned with the strategic influencer, nodes subject to opposing influence should be avoided, but when many nodes are aligned, an optimal influencer should shadow opposing influence.
2051-1310
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 (2018) Effects of time horizons on Influence maximization in the voter dynamics. Journal of Complex Networks. (In Press)

Record type: Article

Abstract

In this paper we analyze influence maximization in the voter model with an active strategic and a passive influencing party in non-stationary settings. We thus explore the dependence of optimal influence allocation on the time horizons of the strategic influencer. We find that on undirected heterogeneous networks, for short time horizons, influence is maximized when targeting low degree nodes, while for long time horizons influence maximization is achieved when controlling hub nodes. Furthermore, we show that for short and intermediate time scales influence maximization can exploit knowledge of (transient) opinion configurations. More in detail, we find two rules. First, nodes with states differing from the strategic influencer’s goal should be targeted. Second, if only few nodes are initially aligned with the strategic influencer, nodes subject to opposing influence should be avoided, but when many nodes are aligned, an optimal influencer should shadow opposing influence.

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effects-time-horizons-revised - Accepted Manuscript
Restricted to Repository staff only until 4 January 2020.
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Accepted/In Press date: 4 October 2018

Identifiers

Local EPrints ID: 425050
URI: https://eprints.soton.ac.uk/id/eprint/425050
ISSN: 2051-1310
PURE UUID: 09c3954b-7ea3-4dc7-916e-11097beafcdc

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Date deposited: 09 Oct 2018 16:30
Last modified: 09 Oct 2018 16:30

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