Journal ArticleDOI
Binary Opinion Dynamics with Stubborn Agents
Ercan Yildiz,Asuman Ozdaglar,Daron Acemoglu,Amin Saberi,Anna Scaglione +4 more
- Vol. 1, Iss: 4, pp 19
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TLDR
It is shown that the presence of stubborn agents with opposing opinions precludes convergence to consensus; instead, opinions converge in distribution with disagreement and fluctuations.Abstract:
We study binary opinion dynamics in a social network with stubborn agents who influence others but do not change their opinions. We focus on a generalization of the classical voter model by introducing nodes (stubborn agents) that have a fixed state. We show that the presence of stubborn agents with opposing opinions precludes convergence to consensus; instead, opinions converge in distribution with disagreement and fluctuations. In addition to the first moment of this distribution typically studied in the literature, we study the behavior of the second moment in terms of network properties and the opinions and locations of stubborn agents. We also study the problem of optimal placement of stubborn agents where the location of a fixed number of stubborn agents is chosen to have the maximum impact on the long-run expected opinions of agents.read more
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References
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Journal ArticleDOI
Ergodic Theorems for Weakly Interacting Infinite Systems and the Voter Model
Richard Holley,Thomas M. Liggett +1 more
TL;DR: In this article, a theorem exhibiting the duality between certain infinite systems of interacting stochastic processes and a type of branching process is proved, and this duality is then used to study the ergodic properties of the infinite system.
Journal ArticleDOI
Naïve Learning in Social Networks and the Wisdom of Crowds
TL;DR: It is shown that all opinions in a large society converge to the truth if and only if the influence of the most influential agent vanishes as the society grows.
Book ChapterDOI
Influential nodes in a diffusion model for social networks
TL;DR: A natural and general model of influence propagation that is generalizing models used in the sociology and economics communities, and shows that in the decreasing cascade model, a natural greedy algorithm is a 1-1/ e-e approximation for selecting a target set of size k.
Journal ArticleDOI
Lower bounds for covering times for reversible Markov chains and random walks on graphs
TL;DR: For simple random walk on aN-vertex graph, the mean time to cover all vertices is at leastcN log(N), wherec>0 is an absolute constant, deduced from a more general result about stationary finite-state reversible Markov chains.