Rumor propagation with heterogeneous transmission in social networks

TL;DR

This paper investigates how heterogeneous transmission probabilities, based on centrality measures, influence rumor spreading in social networks, revealing that targeted adjustments can enhance or control propagation.

Contribution

It introduces a model where transmission probabilities depend on node centrality, providing new insights into controlling rumor spread in complex networks.

Findings

Spreading improves when transmission is proportional to centrality measures.

Targeted adjustment of central nodes' transmission probabilities can control rumor propagation.

Heterogeneous transmission models better reflect real social network dynamics.

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.