mlsanityData to 14 Oct 2026

Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning

arXiv 2412.18322 ↗First posted 24 Dec 2024cs.LG cs.AIcited by 2

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

Abstract (arXiv)

Graph Mamba, a powerful graph embedding technique, has emerged as a cornerstone in various domains, including bioinformatics, social networks, and recommendation systems. This survey represents the first comprehensive study devoted to Graph Mamba, to address the critical gaps in understanding its applications, challenges, and future potential. We start by offering a detailed explanation of the original Graph Mamba architecture, highlighting its key components and underlying mechanisms. Subsequently, we explore the most recent modifications and enhancements proposed to improve its performance and applicability. To demonstrate the versatility of Graph Mamba, we examine its applications across diverse domains. A comparative analysis of Graph Mamba and its variants is conducted to shed light on their unique characteristics and potential use cases. Furthermore, we identify potential areas where Graph Mamba can be applied in the future, highlighting its potential to revolutionize data analysis in these fields. Finally, we address the current limitations and open research questions associated with Graph Mamba. By acknowledging these challenges, we aim to stimulate further research and development in this promising area. This survey serves as a valuable resource for both newcomers and experienced researchers seeking to understand and leverage the power of Graph Mamba.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Video, motion and generative Mamba models, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 45 of its 104 references that are in the map. First posted on arXiv 24 Dec 2024.

Builds on

45 of 104 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024Topological Deep Learning with State-Space Models: A Mamba Approach for Simplicial Complexes18 Sep
  2. 2024DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs13 Aug
  3. 2024Hamba: Single-view 3D Hand Reconstruction with Graph-guided Bi-Scanning Mamba12 Jul
  4. 2024SpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs with Selective State Spaces17 Jun
  5. 2024HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model22 May
  6. 2024STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Model19 Mar
  7. 2024Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces1 Feb

Other references · 38

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  5. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  6. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  7. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  8. 2022Simplified State Space Layers for Sequence Modelingcited by 788
  9. 2024KAN: Kolmogorov-Arnold Networkscited by 649
  10. 2022Embodied Hands: Modeling and Capturing Hands and Bodies Togethercited by 181
  11. 2023Do We Really Need Complicated Model Architectures For Temporal Networks?cited by 143
  12. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 133
26 more
  1. 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspectivecited by 127
  2. 2022Brain Network Transformercited by 94
  3. 2022A Generalization of ViT/MLP-Mixer to Graphscited by 93
  4. 2022FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generationcited by 67
  5. 2024Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challengescited by 64
  6. 2024State Space Model for New-Generation Network Alternative to Transformers: A Surveycited by 59
  7. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  8. 2024Vision Mamba: A Comprehensive Survey and Taxonomycited by 54
  9. 2024Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Surveycited by 53
  10. 2024A Survey of Mambacited by 39
  11. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 30
  12. 2022Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Modelscited by 29
  13. 2024Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systemscited by 26
  14. 2024Mamba in Vision: A Comprehensive Survey of Techniques and Applicationscited by 18
  15. 2024State Space Models on Temporal Graphs: A First-Principles Studycited by 16
  16. 2024DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Modelscited by 14
  17. 2024What Can We Learn from State Space Models for Machine Learning on Graphs?cited by 13
  18. 2024Graph Domain Adaptation: Challenges, Progress and Prospectscited by 11
  19. 2023PTGB: Pre-Train Graph Neural Networks for Brain Network Analysiscited by 10
  20. 2024GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image Classificationcited by 10
  21. 2024Combining Graph Neural Network and Mamba to Capture Local and Global Tissue Spatial Relationships in Whole Slide Imagescited by 7
  22. 2024Enhancing Long-Range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-Based Sentiment Analysiscited by 3
  23. 2024GrassNet: State Space Model Meets Graph Neural Networkcited by 2
  24. 2024Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamicscited by 2
  25. 2024Mamba Meets Financial Markets: A Graph-Mamba Approach for Stock Price Predictioncited by 2
  26. 2024Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learningcited by 1

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025pLSTM: parallelizable Linear Source Transition Mark networkscited by 2
  2. 2025GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Modelcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Graph Mamba: Towards Learning on Graphs with State Space Models
  2. 2024A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers
  3. 2025Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space
  4. 2025Dual Mamba for Node-Specific Representation Learning: Tackling Over-Smoothing with Selective State Space Modeling
  5. 2024DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models
  6. 2025Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
  7. 2024MambaLRP: Explaining Selective State Space Sequence Models
  8. 2025Graph Learning