TY - JOUR
T1 - Generalized mean-field approximation for the Deffuant opinion dynamics model on networks
AU - Fennell, Susan C.
AU - Burke, Kevin
AU - Quayle, Michael
AU - Gleeson, James P.
N1 - Publisher Copyright:
© 2021 American Physical Society.
PY - 2021/1
Y1 - 2021/1
N2 - When the interactions of agents on a network are assumed to follow the Deffuant opinion dynamics model, the outcomes are known to depend on the structure of the underlying network. This behavior cannot be captured by existing mean-field approximations for the Deffuant model. In this paper, a generalized mean-field approximation is derived that accounts for the effects of network topology on Deffuant dynamics through the degree distribution or community structure of the network. The accuracy of the approximation is examined by comparison with large-scale Monte Carlo simulations on both synthetic and real-world networks.
AB - When the interactions of agents on a network are assumed to follow the Deffuant opinion dynamics model, the outcomes are known to depend on the structure of the underlying network. This behavior cannot be captured by existing mean-field approximations for the Deffuant model. In this paper, a generalized mean-field approximation is derived that accounts for the effects of network topology on Deffuant dynamics through the degree distribution or community structure of the network. The accuracy of the approximation is examined by comparison with large-scale Monte Carlo simulations on both synthetic and real-world networks.
UR - http://www.scopus.com/inward/record.url?scp=85100402757&partnerID=8YFLogxK
U2 - 10.1103/PhysRevE.103.012314
DO - 10.1103/PhysRevE.103.012314
M3 - Article
C2 - 33601529
AN - SCOPUS:85100402757
SN - 2470-0045
VL - 103
SP - 012314
JO - Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
JF - Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
IS - 1
M1 - 012314
ER -