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Undirected probabilistic graphical models represent the conditionaldependencies, or Markov properties, of a collection of random variables.Knowing the sparsity of such a graphical model is valuable for modelingmultivariate distributions and for efficiently performing inference. While theproblem of learning graph structure from data has been studied extensively forcertain parametric families of distributions, most existing methods fail toconsistently recover the graph structure for non-Gaussian data. Here we proposean algorithm for learning the Markov structure of continuous and non-Gaussiandistributions. To characterize conditional independence, we introduce a scorebased on integrated Hessian information from the joint log-density, and weprove that this score upper bounds the conditional mutual information for ageneral class of distributions. To compute the score, our algorithm SINGestimates the density using a deterministic coupling, induced by a triangulartransport map, and iteratively exploits sparse structure in the map to revealsparsity in the graph. For certain non-Gaussian datasets, we show that ouralgorithm recovers the graph structure even with a biased approximation to thedensity. Among other examples, we apply SING to learn the dependencies betweenthe states of a chaotic dynamical system with local interactions.

Learning non-Gaussian graphical models via Hessian scores and triangular transport