  We consider the diffusion of two alternatives in social networks using a
game-theoretic approach. Each individual plays a coordination game with its
neighbors repeatedly and decides which to adopt. As products are used in
conjunction with others and through repeated interactions, individuals are more
interested in their long-term benefits and tend to show trust to others to
maximize their long-term utility by choosing a suboptimal option with respect
to instantaneous payoff. To capture such trust behavior, we deploy
limited-trust equilibrium (LTE) in diffusion process. We analyze the
convergence of emerging dynamics to equilibrium points using mean-field
approximation and study the equilibrium state and the convergence rate of
diffusion using absorption probability and expected absorption time of a
reduced-size absorbing Markov chain. We also show that the diffusion model on
LTE under the best-response strategy can be converted to the well-known linear
threshold model. Simulation results show that when agents behave trustworthy,
their long-term utility will increase significantly compared to the case when
they are solely self-interested. Moreover, the Markov chain analysis provides a
good estimate of convergence properties over random networks.
