# Can I think graph convolution as 2D convolution like images?

Kipf et al described in his paper that we can write graph convolution operation like this:

$$H_{t+1} = AH_tW_t$$

where, $$A$$ is the normalized adjacency matrix, $$H_t$$ is the embedded representation of the nodes and $$W_t$$ is the weight matrix.

Now, can I imagine the same formula as first performing 2D convolution with fixed-size kernel over the whole feature space then multiply the result with the adjacency matrix? If this is the case, I think I can create graph convolution operation just using the Conv2D layer then performing simple matrix multiplication with adjacency matrix using PyTorch.

• – nbro Jul 24 '20 at 19:49