Geometric Scattering Attention Networks
This work addresses a specific issue in graph neural networks for researchers and practitioners, offering an incremental improvement over existing methods.
The paper tackles the problem of oversmoothing in graph representation learning by introducing a new attention-based architecture that adaptively combines scattering and GCN channels, resulting in improved performance in semi-supervised node classification.
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.