This paper works on graph-based semi-supervised learning (more specifically node classification) and proposes to combine a geometric scattering transform into GCNs to overcome the over-smoothing issue of state-of-the-art GCNs for node classification. It also proposes graph residual convolutions to better aggregate the node information. The clarity, novelty, and significance are clearly above the bar of NeurIPS. The authors also did a good job in their rebuttal to address some concerns raised by reviewers. Thus, all of us have agreed to accept this paper for publication!