Efficient and Stable Graph Scattering Transforms via Pruning.

Volume: 44, Issue: 3, Pages: 1232 - 1246
Published: Mar 1, 2022
Abstract
Graph convolutional networks (GCNs) have well-documented performance in various graph learning tasks, but their analysis is still at its infancy. Graph scattering transforms (GSTs) offer training-free deep GCN models that extract features from graph data, and are amenable to generalization and stability analyses. The price paid by GSTs is exponential complexity in space and time that increases with the number of layers. This discourages...
Paper Details
Title
Efficient and Stable Graph Scattering Transforms via Pruning.
Published Date
Mar 1, 2022
Volume
44
Issue
3
Pages
1232 - 1246
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