  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
generalised 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.
