VTeller: Telling the Values Somewhere, Sometime in a Dynamic Network of Urban Systems

Dynamic networks are very common in urban systems today. As data are acquired, unfortunately, they are rarely complete observations of the whole system. It is important to reliably infer the unobserved attribute values anywhere in the graphs, at certain times---either in the past or in the future. Previous work does not sufficiently capture the correlations inherent with graph topology and with time. We propose a machine learning approach using a novel probabilistic graphical model. We devise a series of algorithms to efficiently group the vertices, to learn the model parameters, and to infer the unobserved values for query processing. Furthermore, we propose a method to incrementally and automatically update the model. Finally, we perform an extensive experimental study using two real-world dynamic graph datasets to evaluate our approach.