Why do graph convolutional neural networks use normalized adjacency matrices? It seems that it is common to perform something like the following operation (like in Kipf & Welling, "Semi-Supervised Classification with Graph Convolutional Networks" 2017) to preprocess the adjacency matrix in graph convolutional networks
$$
\hat{A} = D^{-\frac{1}{2}} A D^{-\frac{1}{2}}
$$
where $D$ is the degree matrix and $A$ is the adjacency matrix. Then, it seems like we do
$$
\hat{A} X W
$$
where $X$ is the node data and $W$ is a weight matrix to create the new graph.
What is the reasoning behind preprocessing the adjacency matrix using the degree matrix? Why can't we just do $AXW$?

I adopt the authors notation and use $\tilde A$ for the normalized adjacency matrix.
The largest eigenvalue $\lambda_1$ of the normalized adjacency matrix $\tilde A$ is $\lambda_1 \le 1$. This normalization is done so to prevent exploding values from repeated multiplication over multiple layers. This is what the authors mean when discussing equation 7:

Note that $I_N + D^{-\frac{1}{2}}AD^{-\frac{1}{2}}$ now has eigenvalues in the range $[0, 2]$. Repeated application of this operator can therefore lead to numerical instabilities and exploding/vanishing gradients when used in a deep neural network model. To alleviate this problem, we introduce the following \textit{renormalization trick}: $I_N + D^{-\frac{1}{2}}AD^{-\frac{1}{2}}\rightarrow \tilde{D}^{-\frac{1}{2}}\tilde{A}\tilde{D}^{-\frac{1}{2}}$, with $\tilde{A} = A + I_N$ and $\tilde{D}_{ii} = \sum_j \tilde{A}_{ij}$.

The normalized Laplacian is formed from the normalized adjacency matrix: $\hat L = I - \hat A$. $\hat L$ is positive semidefinite. We can show that the largest eigenvalue is bounded by 1 by using the definition of the Laplacian and the Rayleigh quotient.
$$
x^T (I - \tilde A)x \ge 0 \implies 1 \ge \frac{x^T \tilde A x}{x^T x}
$$
This works because $A$ (and therefore $\tilde A$) is symmetric, which is one of the assumptions stated in the paper.
