mlsanityData to 7 Oct 2026

TIDE: Time Derivative Diffusion for Deep Learning on Graphs

arXiv 2212.02483 ↗First posted 5 Dec 2022cs.LG cs.CV cs.SIcited by 17

Not on the map. Why?

Abstract (arXiv)

A prominent paradigm for graph neural networks is based on the message-passing framework. In this framework, information communication is realized only between neighboring nodes. The challenge of approaches that use this paradigm is to ensure efficient and accurate long-distance communication between nodes, as deep convolutional networks are prone to oversmoothing. In this paper, we present a novel method based on time derivative graph diffusion (TIDE) to overcome these structural limitations of the message-passing framework. Our approach allows for optimizing the spatial extent of diffusion across various tasks and network channels, thus enabling medium and long-distance communication efficiently. Furthermore, we show that our architecture design also enables local message-passing and thus inherits from the capabilities of local message-passing approaches. We show that on both widely used graph benchmarks and synthetic mesh and graph datasets, the proposed framework outperforms state-of-the-art methods by a significant margin

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 37 of its 48 references that are in the map. First posted on arXiv 5 Dec 2022.

Builds on

37 of 48 references.

References · 37

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018Neural Ordinary Differential Equationscited by 3,733
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  7. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  8. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  9. 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,437
  10. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  11. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  12. 2020Simple and Deep Graph Convolutional Networkscited by 857
25 more
  1. 2020On the Bottleneck of Graph Neural Networks and its Practical Implicationscited by 624
  2. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  3. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 442
  4. 2019Augmented Neural ODEscited by 396
  5. 2018Graph Convolutional Networks for Text Classificationcited by 345
  6. 2020Improving Graph Neural Network Expressivity via Subgraph Isomorphism Countingcited by 312
  7. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 309
  8. 2020Towards Deeper Graph Neural Networkscited by 296
  9. 2020Scalable Gradients for Stochastic Differential Equationscited by 264
  10. 2021GRAND: Graph Neural Diffusioncited by 258
  11. 2020How to train your neural ODE: the world of Jacobian and kinetic regularizationcited by 228
  12. 2019Graph Matching Networks for Learning the Similarity of Graph Structured Objectscited by 202
  13. 2019Graph Neural Networks with convolutional ARMA filterscited by 185
  14. 2022Revisiting Heterophily For Graph Neural Networkscited by 169
  15. 2020DiffusionNet: Discretization Agnostic Learning on Surfacescited by 163
  16. 2018Adaptive Graph Convolutional Neural Networkscited by 155
  17. 2019Graph Neural Ordinary Differential Equationscited by 153
  18. 2019Graph Wavelet Neural Networkcited by 131
  19. 2019Continuous Graph Neural Networkscited by 114
  20. 2019Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noisecited by 98
  21. 2019Attributed Graph Clustering via Adaptive Graph Convolutioncited by 96
  22. 2021Beltrami Flow and Neural Diffusion on Graphscited by 84
  23. 2019Discrete and Continuous Deep Residual Learning Over Graphscited by 13
  24. 2020Wavelet-based Heat Kernel Derivatives: Towards Informative Localized Shape Analysiscited by 8
  25. 2021Geometric Multimodal Deep Learning with Multi-Scaled Graph Wavelet Convolutional Networkcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 7; 2025: 6; 2026: 3.

17 papers.

Sort:

By area:

  1. 2023From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyondcited by 26
  2. 2024Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theorycited by 8
  3. 2024Continuous Product Graph Neural Networkscited by 8
  4. 2024Synchronous Diffusion for Unsupervised Smooth Non-Rigid 3D Shape Matchingcited by 7
  5. 2024Graph Fourier Neural Kernels (G-FuNK): Learning Solutions of Nonlinear Diffusive Parametric PDEs on Multiple Domainscited by 5
  6. 2026Spatiotemporal Imputation with Graph-Informed Flow Matchingcited by 3
  7. 2024Graph Neural Aggregation-diffusion with Metastabilitycited by 2
  8. 2025Continuous Simplicial Neural Networkscited by 2
  9. 2026Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priorscited by 2
  10. 2026SGMatch: Semantic-Guided Non-Rigid Shape Matching with Flow Regularizationcited by 2
  11. 2024Spatiotemporal Forecasting Meets Efficiency: Causal Graph Process Neural Networkscited by 1
  12. 2025Measuring Over-smoothing beyond Dirichlet energycited by 1
5 more
  1. 2024Smoothed Graph Contrastive Learning via Seamless Proximity Integrationcited by 0
  2. 2025Sinkhorn Normalization of Diffusion Kernelscited by 0
  3. 2025Second-Order Tensorial Partial Differential Equations on Graphscited by 0
  4. 2025Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communicationcited by 0
  5. 2025Rethinking Message Passing Neural Networks with Diffusion Distance-guided Stress Majorizationcited by 0