mlsanityData to 7 Oct 2026

Continuous Product Graph Neural Networks

arXiv 2405.18877 ↗First posted 29 May 2024cs.LG cs.AIcited by 8

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Abstract (arXiv)

Processing multidomain data defined on multiple graphs holds significant potential in various practical applications in computer science. However, current methods are mostly limited to discrete graph filtering operations. Tensorial partial differential equations on graphs (TPDEGs) provide a principled framework for modeling structured data across multiple interacting graphs, addressing the limitations of the existing discrete methodologies. In this paper, we introduce Continuous Product Graph Neural Networks (CITRUS) that emerge as a natural solution to the TPDEG. CITRUS leverages the separability of continuous heat kernels from Cartesian graph products to efficiently implement graph spectral decomposition. We conduct thorough theoretical analyses of the stability and over-smoothing properties of CITRUS in response to domain-specific graph perturbations and graph spectra effects on the performance. We evaluate CITRUS on well-known traffic and weather spatiotemporal forecasting datasets, demonstrating superior performance over existing approaches. The implementation codes are available at https://github.com/ArefEinizade2/CITRUS.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 32 of its 48 references that are in the map. First posted on arXiv 29 May 2024.

Builds on

32 of 48 references.

References · 32

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  3. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  4. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 433
  5. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 409
  6. 2023A Survey on Oversmoothing in Graph Neural Networkscited by 246
  7. 2020A Note on Over-Smoothing for Graph Neural Networkscited by 245
  8. 2016Tensor Decomposition for Signal Processing and Machine Learningcited by 223
  9. 2020DiffusionNet: Discretization Agnostic Learning on Surfacescited by 163
  10. 2019Continuous Graph Neural Networkscited by 114
  11. 2017Geometric Matrix Completion with Recurrent Multi-Graph Neural Networkscited by 88
  12. 2022On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networkscited by 63
20 more
  1. 2022Understanding convolution on graphs via energiescited by 58
  2. 2020Gated Graph Recurrent Neural Networkscited by 57
  3. 2022Scalable Spatiotemporal Graph Neural Networkscited by 52
  4. 2023Graph Signal Processing: History, Development, Impact, and Outlookcited by 39
  5. 2022On the Robustness of Graph Neural Diffusion to Topology Perturbationscited by 30
  6. 2018Forecasting Time Series with VARMA Recursions on Graphscited by 27
  7. 2023From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyondcited by 26
  8. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  9. 2022TIDE: Time Derivative Diffusion for Deep Learning on Graphscited by 17
  10. 2020Multi-way Graph Signal Processing on Tensors: Integrative analysis of irregular geometriescited by 12
  11. 2024Gegenbauer Graph Neural Networks for Time-varying Signal Reconstructioncited by 12
  12. 2015Hierarchical Coupled Geometry Analysis for Neuronal Structure and Activity Pattern Discoverycited by 10
  13. 2020Product Graph Learning from Multi-domain Data with Sparsity and Rank Constraintscited by 10
  14. 2022Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysiscited by 10
  15. 2023Graph Tensor Networks: An Intuitive Framework for Designing Large-Scale Neural Learning Systems on Multiple Domainscited by 10
  16. 2021Graph-Time Convolutional Neural Networkscited by 8
  17. 2020Multi-Graph Tensor Networkscited by 5
  18. 2022Learning Product Graphs from Spectral Templatescited by 5
  19. 2022ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregationcited by 5
  20. 2018Sparse Sampling for Inverse Problems with Tensorscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2026Spatiotemporal Imputation with Graph-Informed Flow Matchingcited by 3
  2. 2025Continuous Simplicial Neural Networkscited by 2
  3. 2025On topological descriptors for graph productscited by 2
  4. 2025Learning Time-Varying Graph Signals via Koopmancited by 1
  5. 2026Dynamic Sheaf Diffusion Networks with Adaptive Local Structure for Heterogeneous Spatio-Temporal Graph Learningcited by 1
  6. 2025Lightweight and Interpretable Transformer via Mixed Graph Algorithm Unrolling for Traffic Forecastcited by 0
  7. 2025Second-Order Tensorial Partial Differential Equations on Graphscited by 0
  8. 2026Full-Spectrum Graph Neural Networks: Expressive and Scalablecited by 0