mlsanityData to 7 Oct 2026

G-Signatures: Global Graph Propagation With Randomized Signatures

arXiv 2302.08811 ↗First posted 17 Feb 2023cs.LGcited by 4

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Abstract (arXiv)

Graph neural networks (GNNs) have evolved into one of the most popular deep learning architectures. However, GNNs suffer from over-smoothing node information and, therefore, struggle to solve tasks where global graph properties are relevant. We introduce G-Signatures, a novel graph learning method that enables global graph propagation via randomized signatures. G-Signatures use a new graph conversion concept to embed graph structured information which can be interpreted as paths in latent space. We further introduce the idea of latent space path mapping. This allows us to iteratively traverse latent space paths, and, thus globally process information. G-Signatures excel at extracting and processing global graph properties, and effectively scale to large graph problems. Empirically, we confirm the advantages of G-Signatures at several classification and regression tasks.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 59 of its 97 references that are in the map. First posted on arXiv 17 Feb 2023.

Builds on

59 of 97 references.

References · 59

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38 more
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50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 2.

4 papers.

  1. 2024Universal randomised signatures for generative time series modellingcited by 4
  2. 2024Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detectioncited by 1
  3. 2026Generating Financial Time Series by Matching Random Convolutional Featurescited by 1
  4. 2026Breaking the Bottlenecks: Scalable Diffusion Models for 3D Molecular Generationcited by 0