Pruned Graph Scattering Transforms

ICLR 2020
Published: Apr 30, 2020
Abstract
Graph convolutional networks (GCNs) have achieved remarkable performance in a variety of network science learning tasks. However, theoretical analysis of such approaches is still at its infancy. Graph scattering transforms (GSTs) are non-trainable deep GCN models that are amenable to generalization and stability analyses. The present work addresses some limitations of GSTs by introducing a novel so-termed pruned (p)GST approach. The resultant...
Paper Details
Title
Pruned Graph Scattering Transforms
Published Date
Apr 30, 2020
Journal
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