Graph Scattering beyond Wavelet Shackles

• URL: http://arxiv.org/abs/2301.11456v1
• Date: Thu, 26 Jan 2023 23:02:38 GMT
• Title: Graph Scattering beyond Wavelet Shackles
• Authors: Christian Koke, Gitta Kutyniok
• Abstract summary: This work develops a flexible and mathematically sound framework for the design and analysis of graph scattering networks. New methods of graph-level feature aggregation are introduced and stability of the resulting composite scattering architectures is established.
• Score: 3.2971341821314777
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 cattering networks conforming to the developed theory perform better than traditional graph-wavelet based scattering approaches in social network graph classification tasks and significantly outperform other graph-based learning approaches to regression of quantum-chemical energies on QM7.

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