Dynamics of Opinions with Bounded Confidence in Social Cliques: Emergence of Fluctuations
Jiangbo Zhang, Deming Yuan, Lei Wang, Claudio Altafini, Guodong Shi

TL;DR
This paper investigates opinion evolution in social networks with bounded confidence within cliques, revealing conditions for convergence, the possibility of persistent fluctuations, and the impact of clique-based interactions on opinion dynamics.
Contribution
It extends classical opinion models by incorporating clique-based local averaging and proves the occurrence of opinion fluctuations with positive probability.
Findings
Opinions converge under certain conditions.
Fluctuations in opinions can persist indefinitely.
Clique interactions fundamentally alter opinion dynamics.
Abstract
In this paper, we study the evolution of opinions over social networks with bounded confidence in social cliques. Node initial opinions are independently and identically distributed; at each time step, nodes review the average opinions of a randomly selected local clique. The clique averages may represent local group pressures on peers. Then nodes update their opinions under bounded confidence: only when the difference between an agent individual opinion and the corresponding local clique pressure is below a threshold, this agent opinion is updated according to the DeGroot rule as a weighted average of the two values. As a result, this opinion dynamics is a generalization of the classical Deffuant-Weisbuch model in which only pairwise interactions take place. First of all, we prove conditions under which all node opinions converge to finite limits. We show that in the limits the event…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Complex Network Analysis Techniques · Misinformation and Its Impacts
