Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model
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.
The paper introduces a stochastic kinetic model that is combined with a variational inference algorithm in order to model social dynamics on an individual centre. The authors initially contrast Hidden Markov models and Stochastic Kinetic Models, before formally deriving their model. The essential benefit of the model is that it scales linearly with the number of observed individuals.  The proposed algorithm is evaluated based on empirical data on epidemy dynamics. Compared to sampling approaches, the novel model and its variants promise superior performance both in terms of efficiency and accuracy.
Overall, given the results, the paper presents a promising approach for evaluating social dynamics in large data sets as an alternative to sampling approaches.  The paper is densely written, making it hard to follow at times. There are quite a number of grammatical errors/typos, which make reading the formal sections of the paper challenging, especially if not strongly acquainted with the area. A good example would have helped to guide the reader.  Some typos that I noticed (I eventually stopped noting them down): - Line 55: `... in the large social systems.' (remove the) - Line 70: CHMM has not been introduced as an acronym - Line 72: `... defnied ...' - Line 98: `... probability of (an) event.' - Check paragraph starting in Line 103 for readability.  - Line 107: Is `Variational Inference to ....' correct (Given the use in the remainder of the text, should it be perhaps be `for' or `with'?) - Line 196-198: Check sentence for correctness. - Line 200: `symptom' Missing `s'? - Line 229: `(explain the task later)' Is that a comment? - Table 1: `Poeple' --> People   The evaluation exclusively concentrates on the comparison to sampling techniques (Gibbs, particle filtering). It would be worthwhile to compare it to other approaches introduced as part of the related work in order to get a more differentiated comparison. Moreover, how does the model perform if you pick larger or smaller subsets than the chosen 20% and 10% of the evaluated datasets? (In other words, why did you choose those numbers?)  Overall, the paper makes interesting promises, but those should be better contextualised with the status quo of the field, and be presented in a clearer fashion.