Quantitative Relative Judgment Aggregation (QRJA) is a new research topic in (computational) social choice. In the QRJA model, agents provide judgments on the relative quality of different candidates, and the goal is to aggregate these judgments across allagents.

Let (X, Y) XY = N(0, XY) bejointly variables. Thisrateisinline(3), which boundmeaningfulk-SMIisitself k-SMIdecompositionGiven in Gaussian 36,37], we k-SMIintoa(X, Y) µXY 2 P(Rdx Rdy), let(X ,Y ) XY :=N(0, XY)bejointly (X, Y).

The central question is,therefore, tounderstand which noise distribution optimizes the privacy-accuracy trade-off, especially when the dimension of the answer vector ishigh.

Graph Neural Networks (GNNs) have been an active research field for the last ten years with significant advancements in graph representation learning [1, 2, 3, 4].