we used the architecture deployed by the authors of [@coma]. The number of parameters in our case is slightly larger due to the fact that the immediate neighbours, that affect the size of the spiral, range from 7 to 10, while the polynomials used in [@coma] go up to the 6th power of the Laplacian. For both datasets, as clearly illustrated in Fig \[fig:spectral\_spiral\_comparison\], spiral convolution-based autoencoders consistently outperform the spectral ones for every latent dimension, in accordance with the analysis made in section \[spiral\_theory\]. Additionally, increasing the latent dimensions, our model’s performance increases at a higher rate than its counterpart. Notice that the number of parameters scales the same way as the latent size grows, but the spiral model makes better use of the added parameters especially looking at dimensions 16, 32, 64, and 128. Especially on the COMA dataset, the spectral model seems to be flattening between 64 and 128 while the spiral is still noticeably decreasing.

### Spiral vs Attention based Convolutions {#attention_comaparison}

In this experiment we compare our method with certain state-of-the-art soft-attention based Graph Neural Networks: **MoNet**: the patch-operator based model of [@monti2017geometric], where the attention weights are the learnable parameters of gaussian kernels defined 