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Journal ArticleDOI

Binary Opinion Dynamics with Stubborn Agents

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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.

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Journal ArticleDOI

Active Sensing of Social Networks

TL;DR: In this paper, an active sensing method to estimate the relative weight (or trust) agents place on their neighbors' information in a social network is proposed, where the stubborn agents excite the system and the latter can be estimated through the reverberation observed from the analysis of the agents' opinions.
Journal ArticleDOI

Distributed randomized algorithms for opinion formation, centrality computation and power systems estimation: A tutorial overview☆

TL;DR: This tutorial paper studies three specific applications: opinion formation in social networks, centrality measures in complex networks and estimation problems in large-scale power systems.
Journal ArticleDOI

Blind Community Detection From Low-Rank Excitations of a Graph Filter

TL;DR: The paper shows that communities can be detected by applying a spectral method to the covariance matrix of graph signals, and indicates that the community detection performance depends on an intrinsic ‘low-pass’ property of the graph filter.
Journal ArticleDOI

Exploring the Role of Social Media and Individual Behaviors in Flood Evacuation Processes: An Agent-Based Modeling Approach

TL;DR: In this paper, the authors developed a general opinion dynamics model to simulate how individuals update their flood hazard awareness when exposed to multiple information sources, including global broadcast, social media, and observations of neighbors' actions.
Posted Content

Distributed Optimization Under Adversarial Nodes

TL;DR: A secure distributed optimization algorithm is proposed that guarantees that the nonadversarial nodes converge to the convex hull of the minimizers of their local functions under the certain conditions on the graph topology, regardless of the actions of a certain number of the adversarial nodes.
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.
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