  Social dynamics is concerned primarily with interactions among individuals
and the resulting group behaviors, modeling the temporal evolution of social
systems via the interactions of individuals within these systems. In
particular, the availability of large-scale data from social networks and
sensor networks offers an unprecedented opportunity to predict state-changing
events at the individual level. Examples of such events include disease
transmission, opinion transition in elections, and rumor propagation. Unlike
previous research focusing on the collective effects of social systems, this
study makes efficient inferences at the individual level. In order to cope with
dynamic interactions among a large number of individuals, we introduce the
stochastic kinetic model to capture adaptive transition probabilities and
propose an efficient variational inference algorithm the complexity of which
grows linearly --- rather than exponentially --- with the number of
individuals. To validate this method, we have performed epidemic-dynamics
experiments on wireless sensor network data collected from more than ten
thousand people over three years. The proposed algorithm was used to track
disease transmission and predict the probability of infection for each
individual. Our results demonstrate that this method is more efficient than
sampling while nonetheless achieving high accuracy.
