Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model

TL;DR

This paper introduces a variational inference method using a stochastic kinetic model to predict individual social dynamics efficiently, demonstrated through epidemic tracking in large sensor network data.

Contribution

It presents a novel stochastic kinetic model combined with an efficient variational inference algorithm for individual-level social dynamic predictions.

Findings

Efficient inference complexity grows linearly with individuals

High accuracy in predicting disease transmission at the individual level

Outperforms sampling-based methods in efficiency

Abstract

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.