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Raphael Carpintero Perez, Sébastien da Veiga, Josselin Garnier, and Brian Staber proposed a Gaussian process regression strategy for inputs consisting of large, sparse graphs with continuous node attributes, where outputs are signals defined on the nodes of the associated inputs.

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