Spatio-Temporal Graphical Model Selection
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We consider the problem of estimating the topology of spatial interactions in a discrete state, discrete time spatio-temporal graphical model where the interactions affect the temporal evolution of each agent in a network. Among other models, the susceptible, infected, recovered (SIR) model for interaction events fall into this framework. We pose the problem as a structure learning problem and solve it using an ℓ_1-penalized likelihood convex program. We evaluate the solution on a simulated spread of infectious over a complex network. Our topology estimates outperform those of a standard spatial Markov random field graphical model selection using ℓ_1-regularized logistic regression.
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