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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Journal ArticleDOI
Finite Time Bounds for Stochastic Bounded Confidence Dynamics
TL;DR: In this paper , a stochastic bounded condence (SBC) model for opinion evolution in social networks is proposed and analyzed using a two-agent system and a bistar graph.
Proceedings ArticleDOI
Targeting interventions for displacement minimization in opinion dynamics
TL;DR: In this paper , a min-max problem was formulated and solved for opinion displacement minimization on a social network, where a social planner (the defender) aims at selecting the optimal network intervention within her given budget constraint in order to minimize the opinion displacement in the system that an adversary (the attacker) is instead trying to maximize.
Journal ArticleDOI
Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach
TL;DR: In this article, a suite of unsupervised classification methods based on experimental design approaches is proposed to select the subsets of events which minimize different measures of mean estimation error, and the associated objective functions are weakly submodular, which allows efficient approximation algorithms with guarantees.
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Graph structure based Heuristics for Optimal Targeting in Social Networks
TL;DR: In this article, a dynamic model for competition in a social network is considered, where two strategic agents have fixed beliefs and the non-strategic/regular agents adjust their states according to a distributed consensus protocol.
Journal ArticleDOI
Graph Structure-Based Heuristics for Optimal Targeting in Social Networks
TL;DR: In this paper , the authors consider a dynamic model for competition in a social network, where two strategic agents have fixed beliefs, and the nonstrategic/regular agents adjust their states according to a distributed consensus protocol.
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