Effects of Time Horizons on Influence Maximization in the Voter Dynamics

TL;DR

This paper investigates how the optimal influence strategy in voter models varies with different time horizons, revealing that targeting low-degree nodes is best for short-term influence, while hub nodes are more effective for long-term influence in heterogeneous networks.

Contribution

It introduces a detailed analysis of influence maximization strategies in voter models considering varying time horizons and network heterogeneity, highlighting new rules for influence targeting.

Findings

Short-term influence favors low-degree nodes.

Long-term influence favors hub nodes.

Knowledge of opinion states improves influence strategies.

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.