Several recent Graph Neural Network (GNN) models propose removing message passing to simplify the network. These models can be generally defined as: $\mathbf{Z} = MLP_{\theta}(f(\mathbf{A}) MLP_{\phi}(\mathbf{X}) )$ where $MLP$ denotes a dense neural network and $f$ is some deterministic function. For example, Wu defines $f$ as: $f(\mathbf{A}) = \tilde{\mathbf{A}}^k$. The intuition is that trainable parameters are not required in the convolution layer. Rather, a dense layer can be applied at the start and end of the network, and a deterministic convolutional layer in the middle can be used to incorporate the graph structure. These models have been proven to outperform parameterized message passing models on many classification benchmarks.