Maximizing the Spread of an Opinion in Few Steps: Opinion Diffusion in Non-Binary Networks

Bredereck, Robert; Jacobs, Lilian; Kellerhals, Leon

doi:10.24963/ijcai.2020/225

Cited by 7 publications

(10 citation statements)

References 7 publications

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“…In [4] a co-evolutionary model is investigated, where also the innate opinion may change over time. There is also substantial work on understanding opinion diffusion, i.e., the process of how opinions spread in a social network [2,17,18,19,29,35]. Moreover, in [23,24] a framework and a simulator for agent-based opinion formation models is presented.…”

Section: Synchronous Hkss On Complete Networkmentioning

confidence: 99%

Asynchronous Opinion Dynamics in Social Networks

Berenbrink¹,

Hoefer²,

Kaaser³

et al. 2022

Preprint

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Opinion spreading in a society decides the fate of elections, the success of products, and the impact of political or social movements. The model by Hegselmann and Krause is a well-known theoretical model to study such opinion formation processes in social networks. In contrast to many other theoretical models, it does not converge towards a situation where all agents agree on the same opinion. Instead, it assumes that people find an opinion reasonable if and only if it is close to their own. The system converges towards a stable situation where agents sharing the same opinion form a cluster, and agents in different clusters do not influence each other.We focus on the social variant of the Hegselmann-Krause model where agents are connected by a social network and their opinions evolve in an iterative process. When activated, an agent adopts the average of the opinions of its neighbors having a similar opinion. By this, the set of influencing neighbors of an agent may change over time. To the best of our knowledge, social Hegselmann-Krause systems with asynchronous opinion updates have only been studied with the complete graph as social network. We show that such opinion dynamics with random agent activation are guaranteed to converge for any social network. We provide an upper bound of O(n|E| 2 (ε/δ) 2 ) on the expected number of opinion updates until convergence, where |E| is the number of edges of the social network. For the complete social network we show a bound of O(n 3 (n 2 + (ε/δ) 2 )) that represents a major improvement over the previously best upper bound of O(n 9 (ε/δ) 2 ). Our bounds are complemented by simulations that indicate asymptotically matching lower bounds.

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Section: Synchronous Hkss On Complete Networkmentioning

confidence: 99%

Asynchronous Opinion Dynamics in Social Networks

Berenbrink¹,

Hoefer²,

Kaaser³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Considering more than two opinions allows to consider thresholds and in particular majority update rules or even averaging operators in the case of continuous numbers as opinions [21,2,3]. Bredereck et al [8] show that any sequence of majority updates is finite when given k ∈ N opinions. Faliszewski et al [16] consider rankings as opinions and establish among other results that following majority updates converges.…”

Section: Rulementioning

confidence: 99%

“…(Section 5) In line with many works on opinion diffusion we study the maximal spread of one particular opinion. While for the case of two opinions a simple greedy approach guarantees a stable state with a maximal spread of one opinion [18], the problem of finding such a sequence when three or more opinions are present becomes NP-hard for strict majority updates even on very restricted graph structures [8]. In addition, Auletta et al [2] show that this problem is hard for three opinions when using weak majority updates but identify some tractable graph structures.…”

Section: Rulementioning

confidence: 99%

Single-Peaked Opinion Updates

Bredereck¹,

George²,

Israel³

et al. 2022

Preprint

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We consider opinion diffusion for undirected networks with sequential updates when the opinions of the agents are single-peaked preference rankings. Our starting point is the study of preserving single-peakedness. We identify voting rules that, when given a single-peaked profile, output at least one ranking that is single peaked w.r.t. a singlepeaked axis of the input. For such voting rules we show convergence to a stable state of the diffusion process that uses the voting rule as the agents' update rule. Further, we establish an efficient algorithm that maximises the spread of extreme opinions.

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“…and p ∞ = lim t→∞ T t p (4) . Following Equation ( 2), after expressing the limiting opinion as the weighted average of the n+1 agents' initial opinions, that is…”

Section: Models Of Opinion Formationmentioning

confidence: 99%

“…Apart from the above works, recent years have seen a surge in the popularity of various forms of opinion diffusion in artificial intelligence [5,16,9,8,13,3,14,7,2]. Some of them also tackled influence maximisation problems [1,4]. Unlike ours and the aforementioned ones, they study how the sequence of opinion update affects the influencing effort.…”

Section: Related Workmentioning

confidence: 99%

Maximising the Influence of Temporary Participants in Opinion Formation

Zhang¹,

Wang²,

Wang³

et al. 2022

Preprint

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DeGroot-style opinion formation presume a continuous interaction among agents of a social network. Hence, it cannot handle agents external to the social network that interact only temporarily with the permanent ones. Many real-world organisations and individuals fall into such a category. For instance, a company tries to persuade as many as possible to buy its products and, due to various constraints, can only exert its influence for a limited amount of time. We propose a variant of the DeGroot model that allows an external agent to interact with the permanent ones for a preset period of time. We obtain several insights on maximising an external agent's influence in opinion formation by analysing and simulating the variant.

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Maximizing the Spread of an Opinion in Few Steps: Opinion Diffusion in Non-Binary Networks

Cited by 7 publications

References 7 publications

Asynchronous Opinion Dynamics in Social Networks

Asynchronous Opinion Dynamics in Social Networks

Single-Peaked Opinion Updates

Maximising the Influence of Temporary Participants in Opinion Formation

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