Do any of the current DGL modules support convolutions over homogeneous multigraphs? That is:

- For a pair of nodes i and j (potentially with i = j), there are K_{i,j} \geq 0 edges connecting the nodes.
- Each node i has an initial feature vector v^0_i.
- Each edge (i,j,k) has an initial feature vector u_{i,j,k}.

If not, does anyone have recommendations on where to start implementing one?

A simple example of a graph convolution in this setting, adapted from here is:

v^{t+1}_i = \sigma\left(\sum_{j,k} \text{Concat}(v^t_j, u_{i,j,k})W_c + v^t_i W_s + b\right),

where \sigma is an activation function, W_c, W_s are trainable weight matrices, and b is a trainable bias.

Thank you!