Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks. (arXiv:2003.08414v4 [cs.LG] UPDATED)

Graph convolutional networks (GCNs) have shown promising results in
processing graph data by extracting structure-aware features. This gave rise to
extensive work in geometric deep learning, focusing on designing network
architectures that ensure neuron activations conform to regularity patterns
within the input graph. However, in most cases the graph structure is only
accounted for by considering the similarity of activations between adjacent
nodes, which limits the capabilities of such methods to discriminate between
nodes in a graph. Here, we propose …

arxiv graph networks