  Geometric scattering has recently gained recognition in graph representation
learning, and recent work has shown that integrating scattering features in
graph convolution networks (GCNs) can alleviate the typical oversmoothing of
features in node representation learning. However, scattering often relies on
handcrafted design, requiring careful selection of frequency bands via a
cascade of wavelet transforms, as well as an effective weight sharing scheme to
combine low- and band-pass information. Here, we introduce a new
attention-based architecture to produce adaptive task-driven node
representations by implicitly learning node-wise weights for combining multiple
scattering and GCN channels in the network. We show the resulting geometric
scattering attention network (GSAN) outperforms previous networks in
semi-supervised node classification, while also enabling a spectral study of
extracted information by examining node-wise attention weights.
