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Graph Scattering beyond Wavelet Shackles

Christian Koke · Gitta Kutyniok

Hall J (level 1) #934

Keywords: [ Rigorous Proofs ] [ Geometric Deep Learning ] [ Scattering ] [ Graph Convolutional Networks ] [ Quantum Chemistry ] [ Wavelets ] [ stability guarantees ]

Abstract: This work develops a flexible and mathematically sound framework for the design and analysis of graph scattering networks with variable branching ratios and generic functional calculus filters.Spectrally-agnostic stability guarantees for node- and graph-level perturbations are derived; the vertex-set non-preserving case is treated by utilizing recently developed mathematical-physics based tools. Energy propagation through the network layers is investigated and related to truncation stability. New methods of graph-level feature aggregation are introduced and stability of the resulting composite scattering architectures is established. Finally, scattering transforms are extended to edge- and higher order tensorial input. Theoretical results are complemented by numerical investigations: Suitably chosen scattering networks conforming to the developed theory perform better than traditional graph-wavelet based scattering approaches in social network graph classification tasks andsignificantly outperform other graph-based learning approaches to regression of quantum-chemical energies on QM$7$.

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