mlsanityData to 7 Oct 2026

TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting

arXiv 2109.02474 ↗First posted 25 Aug 2021cs.LG cs.AIcited by 16

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Abstract (arXiv)

This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data. For spatial-temporal attribute entities with topological structure, the space-time is consecutive and unified while each node's current status is influenced by its neighbors' past states over variant periods of each neighbor. Most spatial-temporal neural networks for traffic forecasting study spatial dependency and temporal correlation separately in processing, gravely impaired the spatial-temporal integrity, and ignore the fact that the neighbors' temporal dependency period for a node can be delayed and dynamic. To model this actual condition, we propose TraverseNet, a novel spatial-temporal graph neural network, viewing space and time as an inseparable whole, to mine spatial-temporal graphs while exploiting the evolving spatial-temporal dependencies for each node via message traverse mechanisms. Experiments with ablation and parameter studies have validated the effectiveness of the proposed TraverseNet, and the detailed implementation can be found from https://github.com/nnzhan/TraverseNet.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 34 of its 54 references that are in the map. First posted on arXiv 25 Aug 2021.

Builds on

34 of 54 references.

References · 34

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22 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 9; 2024: 2; 2025: 1; 2026: 1.

16 papers.

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  1. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  2. 2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
  3. 2022Scalable Spatiotemporal Graph Neural Networkscited by 52
  4. 2023Taming Local Effects in Graph-based Spatiotemporal Forecastingcited by 37
  5. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  6. 2022Sparse Graph Learning from Spatiotemporal Time Seriescited by 22
  7. 2023Neurosymbolic AI for Reasoning over Knowledge Graphs: A Surveycited by 21
  8. 2023Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detectioncited by 17
  9. 2023Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecastingcited by 16
  10. 2024Graph-based Forecasting with Missing Data through Spatiotemporal Downsamplingcited by 15
  11. 2025Nearest Neighbor Multivariate Time Series Forecastingcited by 3
  12. 2023DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecastingcited by 2
4 more
  1. 2023SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecastingcited by 1
  2. 2024Caformer: Rethinking Time Series Analysis from Causal Perspectivecited by 1
  3. 2023A Generic Approach to Integrating Time into Spatial-Temporal Forecasting via Conditional Neural Fieldscited by 0
  4. 2026AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networkscited by 0