mlsanityData to 7 Oct 2026

SpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs with Selective State Spaces

arXiv 2406.11244 ↗First posted 17 Jun 2024cs.LG cs.AIcited by 7

On the map: State-space sequence models (S4, Mamba) → Video, motion and generative Mamba models

Abstract (arXiv)

Spatio-temporal graph (STG) forecasting is a critical task with extensive applications in the real world, including traffic and weather forecasting. Although several recent methods have been proposed to model complex dynamics in STGs, addressing long-range spatio-temporal dependencies remains a significant challenge, leading to limited performance gains. Inspired by a recently proposed state space model named Mamba, which has shown remarkable capability of capturing long-range dependency, we propose a new STG forecasting framework named SpoT-Mamba. SpoT-Mamba generates node embeddings by scanning various node-specific walk sequences. Based on the node embeddings, it conducts temporal scans to capture long-range spatio-temporal dependencies. Experimental results on the real-world traffic forecasting dataset demonstrate the effectiveness of SpoT-Mamba.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Video, motion and generative Mamba models, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 25 of its 36 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

25 of 36 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Model19 Mar
  2. 2024Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces1 Feb

Other references · 23

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  4. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  5. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  6. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  7. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  8. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
  9. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 525
  10. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 433
  11. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 399
  12. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 298
11 more
  1. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 212
  2. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 209
  3. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 169
  4. 2022Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecastingcited by 149
  5. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 135
  6. 2021Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecastingcited by 67
  7. 2022DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detectioncited by 63
  8. 2023InGram: Inductive Knowledge Graph Embedding via Relation Graphscited by 50
  9. 2021Historical Inertia: A Neglected but Powerful Baseline for Long Sequence Time-series Forecastingcited by 26
  10. 2023Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformerscited by 16
  11. 2023Dynamic Relation-Attentive Graph Neural Networks for Fraud Detectioncited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 2.

7 papers.

  1. 2025On Measuring Long-Range Interactions in Graph Neural Networkscited by 9
  2. 2024DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Modelscited by 8
  3. 2024Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learningcited by 2
  4. 2025Damba-ST: Domain-Adaptive Mamba for Efficient Urban Spatio-Temporal Predictioncited by 2
  5. 2025STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Predictioncited by 1
  6. 2026GAMMA-Net: Adaptive Long-Horizon Traffic Spatio-Temporal Forecasting Model based on Interleaved Graph Attention and Multi-Axis Mambacited by 1
  7. 2026F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecastingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction
  2. 2025HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting
  3. 2024ST-Mamba: Spatial-Temporal Selective State Space Model for Traffic Flow Prediction
  4. 2026ButterMamba: Butterworth-Enhanced Spatial-Temporal Mamba for Efficient Traffic Flow Prediction
  5. 2024Robust Traffic Forecasting against Spatial Shift over Years
  6. 2026PHGNet: Prototype-Guided Hypergraph Construction for Heterogeneous Spatiotemporal Forecasting
  7. 2023DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models
  8. 2024ST-MambaSync: The Complement of Mamba and Transformers for Spatial-Temporal in Traffic Flow Prediction