where H {\displaystyle \mathbf {H} } is the matrix of node representations h u {\displaystyle \mathbf {h} _{u}} , X {\displaystyle \mathbf {X} } is the matrix of node features x u {\displaystyle \mathbf {x} _{u}} , σ ( ⋅ ) {\displaystyle \sigma (\cdot )} is an activation function (e.g., ReLU), A ~ {\displaystyle {\tilde {\mathbf {A} }}} is the graph adjacency matrix with the addition of self-loops, D ~ {\displaystyle {\tilde {\mathbf {D} }}} is the graph degree matrix with the addition of self-loops, and Θ {\displaystyle \mathbf {\Theta } } is a matrix of trainable parameters.