Gossips and Prejudices: Ergodic Randomized Dynamics in Social Networks

Abstract

In this paper we study a novel model of opinion dynamics in social networks, which has two main features. First, agents asynchronously interact in pairs, and these pairs are chosen according to a random process. We refer to this communication model as "gossiping". Second, agents are not completely open-minded, but instead take into account their initial opinions, which may be thought of as their "prejudices". In the literature, such agents are often called "stubborn". We show that the opinions of the agents fail to converge, but persistently undergo ergodic oscillations, which asymptotically concentrate around a mean distribution of opinions. This mean value is exactly the limit of the synchronous dynamics of the expected opinions.

submitted for publication

References in corpus (1)

• On the Role of Zealotry in the Voter Model

Cited by in corpus (8)

• A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part I
• Novel Multidimensional Models of Opinion Dynamics in Social Networks
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• Learning hidden influences in large-scale dynamical social networks: A data-driven sparsity-based approach
• Ergodic Randomized Algorithms and Dynamics over Networks
• Finite-Time Elimination of Disagreement of Opinion Dynamics via Covert Noise
• Community Detection for Gossip Dynamics with Stubborn Agents