Rumor propagation with heterogeneous transmission in social networks

Abstract

Rumor models consider that information transmission occurs with the same probability between each pair of nodes. However, this assumption is not observed in social networks, which contain influential spreaders. To overcome this limitation, we assume that central individuals have a higher capacity of convincing their neighbors than peripheral subjects. By extensive numerical simulations we find that the spreading is improved in scale-free networks when the transmission probability is proportional to PageRank, degree, and betweenness centrality. In addition, the results suggest that the spreading can be controlled by adjusting the transmission probabilities of the most central nodes. Our results provide a conceptual framework for understanding the interplay between rumor propagation and heterogeneous transmission in social networks.

13 pages, 8 figures

References in corpus (8)

• Statistical physics of social dynamics
• The structure and dynamics of multilayer networks
• Adaptive Coevolutionary Networks: A Review
• Theory of Rumour Spreading in Complex Social Networks
• Explosive Synchronization Transitions in Scale-free Networks
• Searching for superspreaders of information in real-world social media
• Traffic-driven Epidemic Spreading in Finite-size Scale-Free Networks
• Emergence of influential spreaders in modified rumor models

Cited by in corpus (4)

• The Impact of Social Curiosity on Information Spreading on Networks
• Controversy-seeking fuels rumor-telling activity in polarized opinion networks
• From spatio-temporal data to chronological networks: An application to wildfire analysis
• Bi-layer voter model: Modeling intolerant/tolerant positions and bots in opinion dynamics