  In this paper, we are interested in modeling the diffusion of information in
a multilayer network using thermodynamic diffusion approach. State of each
agent is viewed as a topic mixture represented by a distribution over multiple
topics. We have observed and learned diffusion-related thermodynamical patterns
in the training data set, and we have used the estimated diffusion structure to
predict the future states of the agents. A priori knowledge of a fraction of
the state of all agents changes the problem to be a Kalman predictor problem
that refines the predicted system state using the error in estimation of the
agents. A real world Twitter data set is then used to evaluate and validate our
information diffusion model.
