where i = top k ( y ) {\displaystyle \mathbf {i} ={\text{top}}_{k}(\mathbf {y} )} is the subset of nodes with the top-k highest projection scores, ⊙ {\displaystyle \odot } denotes element-wise matrix multiplication, and sigmoid ( ⋅ ) {\displaystyle {\text{sigmoid}}(\cdot )} is the sigmoid function. In other words, the nodes with the top-k highest projection scores are retained in the new adjacency matrix A ′ {\displaystyle \mathbf {A} '} . The sigmoid ( ⋅ ) {\displaystyle {\text{sigmoid}}(\cdot )} operation makes the projection vector p {\displaystyle \mathbf {p} } trainable by backpropagation, which otherwise would produce discrete outputs.[18]