Can we add dilated layers to Graph Neural Nets? GNNs are not as deep as CNNs (due to over-smoothing and other factors). So could we replace a graph convolutional layer
$$ h_i^{(l)} = \sigma\left(w^{(l)} \cdot \text{Agg}\left\{ h_j^{(l-1)},\forall j \in N(i)\right\} \right),$$
with a dilated convolutional layer as seen in CNNs to alleviate this problem?
Is this possible? Because nodes are unordered and not in an euclidean geometry, then perhaps not, but, we still apply take information from neighbours instead of pixels, so the layer would perhaps 'skip' some nodes? Because in some settings we might want to take information from the whole spatial space, not just close neighbours.

I googled and found this paper: DeepGCNs: Can GCNs Go as Deep as CNNs?.

but they found that it only works with residual connections:

Without such connections, performance
can actually degrade with dilated graph convolutions. The
reason for this is probably that these varying neighbors result in ‘worse’ gradients, which further hinder convergence
when residual graph connections are not used.

