Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model
This work addresses the challenge of making efficient individual predictions in social dynamics, which is incremental as it builds on prior research focused on collective effects.
The study tackled the problem of predicting individual-level state-changing events like disease transmission in social systems by introducing a stochastic kinetic model with a variational inference algorithm that scales linearly with the number of individuals, achieving high accuracy and efficiency in experiments on wireless sensor network data from over ten thousand people.
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.