This was done by converting the DB schema into a graph, where nodes are DB constants, and edges connect tables and their columns, as well as primary and foreign keys (Figure 2 To focus the GCN's capacity on important nodes, a relevance probability ρ v was computed for every node, and used to "gate" the input to the GCN, conditioned on the question.
given a learned embedding r v for every database constant, the GCN input is h Then, the GCN recurrence is applied for L steps.