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Learning structures between groups of variables from data with missing valuesis an important task in the real world, yet difficult to solve. One typicalscenario is discovering the structure among topics in the education domain toidentify learning pathways. Here, the observations are student performances forquestions under each topic which contain missing values. However, most existingmethods focus on learning structures between a few individual variables fromthe complete data. In this work, we propose VISL, a novel scalable structurelearning approach that can simultaneously infer structures between groups ofvariables under missing data and perform missing value imputations with deeplearning. Particularly, we propose a generative model with a structured latentspace and a graph neural network-based architecture, scaling to a large numberof variables. Empirically, we conduct extensive experiments on synthetic,semi-synthetic, and real-world education data sets. We show improvedperformances on both imputation and structure learning accuracy compared topopular and recent approaches.

Simultaneous Missing Value Imputation and Structure Learning with Groups