Zealotry and Influence Maximization in the Voter Model: When to Target Partial Zealots?

Moreno, Guillermo Romero; Manino, Edoardo; Tran-Thanh, Long; Brede, Markus

doi:10.1007/978-3-030-40943-2_10

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…For an intermediate fields strength (H = 3), we find that the optimal influence is obtained by concentrating the external field on nodes of intermediate degree (rather than dividing it between hubs and leaves). Note that these results are general, and are not limited to the BA network [20,21,23,[28][29][30]. The assumption in previous work was that the qualitative behavior of the optimal field depends mostly on a node's degree and less so on the structure of the network.…”

Section: Influence Maximizationmentioning

confidence: 53%

“…Earlier work [20,21,23,[28][29][30] has shown that the optimal field should be stronger for the high-connectivity nodes where the total field H is small and stronger on the lower-degree nodes where H is large. In figure 4 we plot the normalized optimal field for each node vs the node's degree for three different values of the total field H .…”

Section: Influence Maximizationmentioning

confidence: 97%

“…Such problems are commonly analyzed using the ferromagnetic Ising model described above, where the mean opinion in the network is given by the mean magnetization at thermal equilibrium [20,21,23,[28][29][30]. The main question is then as follows: given a limited budget of external influence (say a limit budget for advertisement), modeled as a limited amount of external field denoted by…”

Section: Influence Maximizationmentioning

confidence: 99%

“…Specifically, we consider the optimal ways to exert influence on a network given limited resources [11,18]. Using the analogy between social networks and the Ising model [11,19] it was recently shown that, to maximize influence, either hubs or leaf nodes should be targeted [20,21]. We validate and expand on these results on a large Barabasi-Albert network [22], and without the mean-field approximation [23], commonly used to analyze such systems.…”

Section: Introductionmentioning

confidence: 98%

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Echo chambers in the Ising model and implications on the mean magnetization

Baravi¹,

Feinerman²,

Raz³

2022

J. Stat. Mech.

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The echo-chamber effect is a common term in opinion dynamic modeling to describe how a person’s opinion might be artificially enhanced as it is reflected back at her through social interactions. Here, we study the existence of this effect in statistical mechanics models, which are commonly used to study opinion dynamics. We show that the Ising model does not exhibit echo-chambers, but this result is a consequence of a special symmetry. We then distinguish between three types of models: (i) those with a strong echo-chamber symmetry, that have no echo-chambers at all; (ii) those with a weak echo-chamber symmetry that can exhibit echo-chambers but only if there are external fields in the system, and (iii) models without echo-chamber symmetry that generically have echo-chambers. We use these results to construct an efficient algorithm to efficiently and precisely calculate magnetization in arbitrary tree networks. Finally, we apply this algorithm to study two systems: phase transitions in the random field Ising model on a Bethe lattice and the influence optimization problem in social networks.

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Section: Influence Maximizationmentioning

confidence: 53%

Section: Influence Maximizationmentioning

confidence: 97%

Section: Influence Maximizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

See 2 more Smart Citations

Echo chambers in the Ising model and implications on the mean magnetization

Baravi¹,

Feinerman²,

Raz³

2022

J. Stat. Mech.

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Strategic Promotional Campaigns for Sustainable Behaviors: Maximizing Influence in Competitive Complex Contagions

Lipiecki

2024

Lecture Notes in Computer Science

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A bibliometric analysis and basic model introduction of opinion dynamics

2022

Appl Intell

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to real sociological studies" in 2009. The complexity also stems from the multidisciplinary trait of this research field. The research on it involves disciplines such as physics, mathematics, computer science, social psychology, and philosophy [2]. Different from reviews mostly based on the perspective of statical physics, a multidisciplinary review discussed the disciplinary origins of this field. It was found that social processes combined with analytical and computational tools resulted in the intricacy of the discipline [3]. This complexity also brings about diversiform and abundant studies from different and multiplex perspectives and access points.With the development of opinion dynamics, increasing attention has been paid to it and 16 reviews on this direction published in recent 5 years. 10 of 16 reviews were published in 2020 and 2021, while there are 20 reviews concerning this research direction in total since the first one was published in 2007. The above-mentioned reviews refer to the type which is recorded as "review" in the Web of Science (WoS) Core Collection database. These reviews are developed from different perspectives. Lorenz gave a sound survey on the continuous opinion dynamics under bounded

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Zealotry and Influence Maximization in the Voter Model: When to Target Partial Zealots?

Cited by 7 publications

References 31 publications

Echo chambers in the Ising model and implications on the mean magnetization

Echo chambers in the Ising model and implications on the mean magnetization

Strategic Promotional Campaigns for Sustainable Behaviors: Maximizing Influence in Competitive Complex Contagions

A bibliometric analysis and basic model introduction of opinion dynamics

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