Why can we perform graph convolution using the standard 2d convolution with $1 times Gamma$ kernels?

Recently I was reading this paper Skeleton Based Action RecognitionUsing Spatio Temporal Graph Convolution. In this paper, the authors claim (below equation (ref{9})) that we can perform graph convolution with the following formula

$$
mathbf{f}_{o u t}=mathbf{Lambda}^{-frac{1}{2}}(mathbf{A}+mathbf{I}) mathbf{Lambda}^{-frac{1}{2}} mathbf{f}_{i n} mathbf{W} label{9}tag{9}
$$

using the standard 2d convolution with kernels of shape $1 times Gamma$ (where $Gamma$ is defined under equation 6 of the paper), and then multiplying it with the normalised adjacency matrix

$$mathbf{Lambda}^{-frac{1}{2}}(mathbf{A}+mathbf{I}) mathbf{Lambda}^{-frac{1}{2}}$$

For the past few days, I was thinking about his claim but I can’t find an answer. Does anyone read this paper and can help me to find it out, please?