Information diffusion in interconnected heterogeneous networks
In this paper, we are interested in modeling the diffusion of information in a multilayer network of agents using a thermodynamic diffusion approach. The state of each agent is viewed as a topic mixture, to describe his/her resources, and represented by a distribution over multiple topics. We observe and learn diffusion-related thermodynamical patterns in the training data set, and we use the estimated diffusion structure to predict the future states of the agents. With a priori knowledge of a fraction of the state of all agents, the problem is shown to turn into a Kalman predictor problem that refines the predicted system states using the estimation error of the agents' states. A real world Twitter data set is then used to evaluate and validate our information diffusion model.