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
Citations
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Proceedings ArticleDOI
On a Modified DeGroot-Friedkin model of opinion dynamics
Zhi Xu,Ji Liu,Tamer Basar +2 more
TL;DR: In this article, a modified version of the DeGroot-Friedkin model is proposed, which allows individuals to update their self-confidence levels by only interacting with their neighbors and allows the update of selfconfidence levels to take place in finite time without waiting for the opinion process to reach consensus on any particular issue.
Proceedings ArticleDOI
Learning a Linear Influence Model from Transient Opinion Dynamics
TL;DR: Novel algorithms to estimate edge influence strengths from an observed series of opinion values at nodes, adopting a linear (but not stochastic) influence model are presented.
Journal ArticleDOI
Message Passing Optimization of Harmonic Influence Centrality
TL;DR: A new measure of node centrality in social networks, the Harmonic Influence Centrality (HIC), which emerges naturally in the study of social influence over networks is proposed using an intuitive analogy between social and electrical networks.
Kinetics of Social Contagion
TL;DR: In this article, the authors study analytically and by simulations a general model that incorporates threshold mechanism capturing sensitivity to peer pressure, the effect of "immune" nodes who never adopt, and a perpetual flow of external information.
Journal ArticleDOI
Characteristics of successful opinion leaders in a bounded confidence model
TL;DR: In this paper, the authors analyzed the impact of competing opinion leaders on attracting followers in a social group based on a bounded confidence model in terms of reputation, stubbornness, appeal and extremeness.
References
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Journal ArticleDOI
A Simple Model of Herd Behavior
TL;DR: In this article, the authors analyze a sequential decision model in which each decision maker looks at the decisions made by previous decision makers in taking her own decision, and they show that the decision rules that are chosen by optimizing individuals will be characterized by herd behavior.
Proceedings ArticleDOI
Maximizing the spread of influence through a social network
TL;DR: An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.
Journal ArticleDOI
A New Product Growth for Model Consumer Durables
TL;DR: A growth model for the timing of initial purchase of new products is developed and tested empirically against data for eleven consumer durables, and a long-range forecast is developed for the sales of color television sets.
Journal ArticleDOI
Maximizing the Spread of Influence through a Social Network
TL;DR: The problem of finding the most influential nodes in a social network is NP-hard as mentioned in this paper, and the first provable approximation guarantees for efficient algorithms were provided by Domingos et al. using an analysis framework based on submodular functions.
Book
Interacting Particle Systems
TL;DR: The construction, and other general results are given in this paper, with values in [0, ] s. The voter model, the contact process, the nearest-particle system, and the exclusion process.