Abstract Simple graph convolution (SGC) achieves competitive classification accuracy to convolutional networks (GCNs) in various tasks while being computationally more efficient and fitting fewer parameters. However, the width of SGC is narrow due over-smoothing with higher power, which limits learning ability representations. Here, we propose AdjMix, a simple attentional model, that scalable w...