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The Generalized Skew Spectrum of Graphs

arXiv 2505.23609 ↗First posted 29 May 2025cs.LG cs.DS math.GRcited by 0

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Abstract (arXiv)

This paper proposes a family of permutation-invariant graph embeddings, generalizing the Skew Spectrum of graphs of Kondor & Borgwardt (2008). Grounded in group theory and harmonic analysis, our method introduces a new class of graph invariants that are isomorphism-invariant and capable of embedding richer graph structures - including attributed graphs, multilayer graphs, and hypergraphs - which the Skew Spectrum could not handle. Our generalization further defines a family of functions that enables a trade-off between computational complexity and expressivity. By applying generalization-preserving heuristics to this family, we improve the Skew Spectrum's expressivity at the same computational cost. We formally prove the invariance of our generalization, demonstrate its improved expressiveness through experiments, and discuss its efficient computation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 37 references that are in the map. First posted on arXiv 29 May 2025.

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References · 9

  1. 2018How Powerful are Graph Neural Networks?cited by 4,393
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  3. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
  4. 2021Automatic Symmetry Discovery with Lie Algebra Convolutional Networkcited by 83
  5. 2019Hierarchical Graph Pooling with Structure Learningcited by 81
  6. 2023Ewald-based Long-Range Message Passing for Molecular Graphscited by 46
  7. 2023From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networkscited by 17
  8. 2023A General Framework for Equivariant Neural Networks on Reductive Lie Groupscited by 16
  9. 2024On the Expressive Power of Spectral Invariant Graph Neural Networkscited by 15

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