期刊
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS
卷 49, 期 3, 页码 1046-1074出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.acha.2019.06.003
关键词
Scattering transform; Graph neural networks; Graph convolution; Spectral graph theory; Wavelets; Permutation invariance; Feature learning
资金
- NSF [DMS-14-18386, DMS-18-21266, DMS-18-30418]
- NGA
- NSF
We generalize the scattering transform to graphs and consequently construct a convolutional neural network on graphs. We show that under certain conditions, any feature generated by such a network is approximately invariant to permutations and stable to signal and graph manipulations. Numerical results demonstrate competitive performance on relevant datasets. (C) 2019 Elsevier Inc. All rights reserved.
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