Pith. sign in

REVIEW 4 major objections 6 minor 104 references

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

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This survey claims to be the first comprehensive study of Graph Mamba, organizing the state-space-model approach to graph learning.

desk verdict A useful but sloppy survey of Graph Mamba; the 'first comprehensive survey' claim is unverified and the transcription errors need fixing before it can serve as a reliable benchmark reference. read the letter →

arxiv 2412.18322 v1 pith:G3IDPEPC submitted 2024-12-24 cs.LG cs.AI

Safa Ben Atitallah , Chaima Ben Rabah , Maha Driss , Wadii Boulila , Anis Koubaa This is my paper · ORCID
classification cs.LGcs.AI
keywords StateSpaceModelsMambaBlockGraphLearningConvolutionalNetworkSelectivescanSpatio-temporalgraphsHeterogeneous
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that Graph Mamba, a family of models combining state-space models with graph-structured data, has matured into a distinct research area deserving its own survey, and that this survey is the first to map it. It argues that Graph Mamba addresses central GNN weaknesses, limited long-range dependency capture and quadratic complexity, by using selective scanning mechanisms that process graphs in linear time. If the survey's organization is sound, it gives newcomers and experts a single starting point for the architecture, its variants, applications in traffic, healthcare, finance, remote sensing, and sentiment analysis, plus benchmarks and open problems.

What carries the argument

The central machinery is the selective scan mechanism inherited from Mamba and the S6 state-space layer: an input-dependent gating of the state-transition matrices $B$, $C$, and $\Delta$, followed by discretization and a linear-time state-space evolution. In Graph Mamba this mechanism is adapted to graphs by treating nodes and edges as sequence elements and deciding which states propagate across the structure. The paper organizes the field around this machinery: state-space-based message passing replaces neighborhood aggregation, graph types determine how the scan is applied, and the scanning strategies (graph selective, temporal dependency, directed, bi-directional, recurrent, parallel) specify how traversal is ordered. The claim that Graph Mamba forms a unified area rests on these models sharing this core mechanism while differing in how they linearize graph structure.

What would settle it

A literature search would falsify the 'first comprehensive study' claim if it finds an earlier Graph Mamba survey; a check of Tables 10 to 14 against the cited primary papers would falsify the benchmark claim if entries such as Table 12's '069' complexity figure or the attributed GAT results do not match the originals.

Watch

Extended reading notes

Core claim

The paper's central claim is that Graph Mamba is a coherent and rapidly growing family of models at the intersection of selective state-space models (S6) and graph learning. Its architecture replaces or augments GNN message passing with state-space-based propagation driven by a selective scan that focuses on the most relevant nodes and edges, enabling long-range spatial and temporal dependency modeling at linear complexity. The survey maps this area through a taxonomy of graph types, dynamic, heterogeneous, and spatio-temporal, along with six scanning mechanisms, graph selective, temporal dependency, directed, bi-directional, recurrent, and parallel, and training strategies. Comparative tables position Graph Mamba variants ahead of LSTM, GCN, attention, and transformer baselines in traffic forecasting, brain classification, hyperspectral image classification, financial prediction, and aspect-based sentiment analysis.

Load-bearing premise

The survey's value rests on the premise that no comparable Graph Mamba survey existed when it was written, and that the numbers in its comparison tables faithfully reproduce the original papers' results.

Editorial extensions

If this is right

  • Graph Mamba models can capture long-range dependencies in graphs with reported linear-time complexity, making them a practical alternative to attention-based GNNs on large dynamic graphs.
  • The taxonomy of scanning mechanisms gives practitioners a menu for adapting state-space models to a given graph type and task, from directed citation networks to bidirectional spatial scans.
  • The benchmark tables position Graph Mamba variants ahead of LSTM, GCN, and transformer baselines in traffic forecasting, brain-activity classification, hyperspectral imaging, stock prediction, and sentiment analysis.
  • The survey's list of open challenges, scalability, interpretability, slow convergence on heterogeneous graphs, and training on temporal graphs, defines a concrete research agenda for the field.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the survey's claim to be first is correct, it may become the organizing reference for this subarea, and its taxonomy of graph types and scanning mechanisms could become a standard vocabulary for describing Graph Mamba models.
  • The comparative tables suggest that selective state-space models may serve as a drop-in substitute for attention in graph tasks where long-range dependencies and linear scaling matter, but the reported gains come from domain-specific adaptations rather than a single universal architecture.
  • A testable extension is a shared benchmark suite that evaluates Graph Mamba variants on the same dynamic, heterogeneous, and spatio-temporal graphs with unified metrics, since the tables compile numbers from papers using different splits and protocols.
  • The repeated emphasis on self-supervised learning as a future direction implies that labeled-graph scarcity, not architecture capacity, may be the next constraint on Graph Mamba adoption.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. This manuscript is a survey of Graph Mamba and, more broadly, state-space models (SSMs) for graph learning. It opens with preliminaries on GNNs, SSMs, and the Mamba architecture; proposes a taxonomy of graph types (dynamic, heterogeneous, spatio-temporal) and six selective-scanning mechanisms; reviews applications in general-purpose graph learning, knowledge/heterogeneous graphs, traffic/environmental forecasting, healthcare and biosignals, remote sensing, finance, and sentiment analysis; lists evaluation metrics and datasets; and provides comparative tables of Graph Mamba variants against baseline models in several domains. The paper closes with challenges and future research directions. Its stated flagship contribution is being the first comprehensive survey devoted to Graph Mamba.

Significance. If its coverage and tables are made reliable, this survey could serve as a useful entry point for researchers entering the area: it organizes a fast-growing literature under an architecture-oriented taxonomy, catalogs scanning mechanisms, summarizes training strategies, and gathers applications and benchmark datasets in one place. The comparative tables (Section 6) and the open-problems discussion (Sections 7 and 8) are the most valuable artifacts. The survey does not claim new derivations, code, or experiments, so its significance rests on accuracy and completeness of the synthesized material. The claim of being the first comprehensive Graph Mamba survey is potentially significant but is not substantiated; several concrete transcription errors currently undercut the paper's usefulness as a benchmark reference.

major comments (4)
  1. [Abstract and Section 1.2] The Abstract and Section 1.2 assert that this is 'the first comprehensive study devoted to Graph Mamba' and that 'there is currently no thorough review' bringing the findings together. Section 1.1 positions the work only against GNN surveys and Mamba surveys; no literature search, database, date range, or comparison with any prior survey on graph state-space models or Graph Mamba is reported. Because 'first' is a comparative factual claim, this is load-bearing for the stated contribution. The authors should either report a documented search (e.g., arXiv, Google Scholar, DBLP with date range and query terms) and discuss any existing graph-SSM surveys, or soften the claim to match what is actually demonstrated.
  2. [Section 6, Tables 9 and 12; Section 2.1.2] The comparative tables, which are a central promised contribution, contain verifiable transcription errors: Table 12 lists GraphMamba's complexity as '069' instead of '0.69'; Table 9 uses 'URL1' through 'URL15' placeholders instead of actual dataset links; and Section 2.1.2 attributes GAT to reference [13] (Hamilton et al., GraphSAGE) while the GAT paper is reference [58]. These errors suggest that the table entries have not been systematically checked against the primary sources. Every row of Tables 10-14 and the dataset table should be re-verified against the original papers, and the citations in Section 2.1.2 should be corrected.
  3. [Sections 2.3, 3.1, and 4.4] The paper never states the inclusion criteria that distinguish 'Graph Mamba' from the broader class of 'state-space models for graph learning,' although the title uses both. Section 3.1 describes Graph Mamba as based on state-space message passing, selective scanning, and spatial-temporal integration, but Section 4.4 includes GraphS4mer [47] and other S4-based models that do not use the Mamba selective-scanning architecture. This boundary matters because the coverage claims and the 'first comprehensive Graph Mamba survey' statement depend on a clear definition. Please state the selection criteria used to include or exclude models in each section.
  4. [Section 6, Tables 10-14] The comparative analysis reports numerical results from different primary papers without stating the provenance of the baseline numbers or whether the same experimental protocols, backbones, and hyperparameter settings were used. For example, Table 10 compares STG-Mamba with LSTM-based methods, STGCN, and STAEformer, but the table does not cite the source of each reported value, and the text concludes that the Graph Mamba model is 'superior' without describing how the comparison was made consistent. The authors should provide a citation for each reported baseline, state whether numbers were taken from the original papers or recomputed, and flag any comparisons that mix results from different experimental setups.
minor comments (6)
  1. [Throughout] There are numerous typographical errors, including 'Nnumerous', 'MAmba', 'Tranformers', 'Grah-Mamba', 'Lincan el al.', and 'el al.' in several table entries. A thorough proofreading pass is needed.
  2. [Section 5.2] The sentence describing fMRI data reads 'brain regions of interest (ROIs.' and is missing the closing parenthesis; this should be corrected.
  3. [Section 7.1.2] The text defines 'GPUs' as 'Graph Processing Units'; the standard expansion is 'Graphics Processing Units,' and the intended meaning in context should be clarified or corrected.
  4. [Section 8.3] The phrase 'Thi will improve the predictive accuracy' should read 'This will improve the predictive accuracy.'
  5. [Section 2.1.1] In Equation (1), the normalization factor c_ij is introduced as 'a normalization factor' but its definition in terms of node degrees is not given; adding the standard definition would improve readability.
  6. [Table 13] The column header 'second/epoch' mixes a full word with a slash; use 'seconds/epoch' or a consistent unit format.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: this survey transcribes primary-source results and contains no fitted-input-as-prediction or self-citation that carries a load-bearing inference.

full rationale

This is a survey, not a derivation. Its contributions—an architecture description, taxonomy of graph types and scanning mechanisms, application summaries, and comparative performance tables—are compiled from the primary literature (e.g., STG-Mamba [20], BrainMamba [33], GraphMamba [34], MambaForGCN [65]) and are not derived from any parameter fitted or defined in this paper. There is no quantity that is fitted to a subset of data and then presented as a prediction, and no uniqueness theorem or modeling ansatz is imported from the authors' prior work to force a conclusion. The self-citations that appear ([35], [44], [45], [62]) are contextual references for smart-city learning, self-supervised learning, and remote sensing; none carries a load-bearing inference, and removing them would not alter the survey's comparative claims. The abstract's assertion of 'the first comprehensive study devoted to Graph Mamba' is an unverified novelty claim, and Section 1.1 reviews only GNN surveys and Mamba surveys without reporting a systematic search for prior Graph Mamba surveys; however, this is an evidentiary gap in a comparative claim, not a circular reduction of a derivation to its own inputs. Likewise, the URL placeholders in Table 9 and the '069' complexity entry in Table 12 are transcription errors, not evidence of circularity. Because no claim in the paper reduces by construction to its own definitions or inputs, the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 1 invented entities

As a survey, the paper introduces no fitted quantities. Its load-bearing assumptions are the completeness of the surveyed corpus, the accuracy of the transcribed benchmark numbers, and the inherited efficiency claims of the surveyed models. The only invented construct is the survey's own taxonomy, which is organizational rather than empirical.

assumptions (3)
  • standard math Standard continuous-time SSM equations (Eqs. 4 and 5) with matrices A, B, C, D are the accepted formulation underlying S4, S5, and S6, and therefore Graph Mamba.
    Section 2.2 states Eqs. 4 and 5 as background without proof, which is appropriate for standard control-theory material that the survey does not need to justify.
  • domain assumption The corpus of roughly 30 papers surveyed in Sections 4 to 6 is representative and sufficiently complete to support 'comprehensive' status, and their reported benchmark numbers are accurate as transcribed.
    The comprehensiveness claim and all comparison tables in Section 6 depend on this assumption. The survey performs no independent re-evaluation, and Table 12's '069' typo shows that transcription errors do occur.
  • domain assumption Mamba and SSM-based graph models inherit linear time complexity and capture long-range dependencies on graph data.
    Sections 2.3 and 3.1 assert linear complexity and long-range dependency modeling as standing features of the family. This is the surveyed literature's claim, repeated as fact rather than demonstrated for each variant.
invented entities (1)
  • The survey's own taxonomy: three graph types (dynamic, heterogeneous, spatio-temporal) and six scanning mechanisms (graph selective, temporal dependency, directed, bidirectional, recurrent, parallel).
    purpose: Organizational scheme for structuring the surveyed literature; it has no predictive or mechanistic role.
    The taxonomy in Sections 3.2 to 3.3 is the authors' construction, presented without evidence that these categories are the natural or complete partition of the field. It is a framing device, not a falsifiable entity.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning." pith.science (2026). https://pith.science/paper/G3IDPEPC

@misc{pith2026241218322,
  author       = {Pith},
  title        = {Pith review of: Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G3IDPEPC}},
  note         = {Machine review of arXiv:2412.18322}
}
read the original abstract

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.

Figures

Figures reproduced from arXiv: 2412.18322 by the authors.

Figure 1
Figure 1. Graph Mamba Architecture: including sequence transformations (Conv and SSM blocks), linear [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Illustration of different graph types analyzed with the Graph Mamba Framework. [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

104 extracted references · 47 canonical work pages

  1. [13]

    Hamilton, Z

    W. Hamilton, Z. Ying, J. Leskovec, Inductive representation learning on large graphs, Advances in neural information processing systems 30 (2017)

    2017

  2. [58]

    Veličković, G

    P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, Y. Bengio, Graph attention networks, arXiv preprint arXiv:1710.10903 (2017)

    arXiv 2017

  3. [47]

    S. Tang, J. A. Dunnmon, Q. Liangqiong, K. K. Saab, T. Baykaner, C. Lee-Messer, D. L. Rubin, Modeling multivariate biosignals with graph neural networks and structured state space models, in: Conference on Health, Inference, and Learning, PMLR, 2023, pp. 50–71

    work page 2023

  4. [1]

    F. Xia, K. Sun, S. Yu, A. Aziz, L. Wan, S. Pan, H. Liu, Graph learning: A survey, IEEE Transactions on Artificial Intelligence 2 (2) (2021) 109–127

    2021

  5. [2]

    Jiang, J

    W. Jiang, J. Luo, Graph neural network for traffic forecasting: A survey, Expert systems with applications 207 (2022) 117921. , Vol. 1, No. 1, Article . Publication date: December 2024. 32 Ben Atitallah et al

    2022

  6. [3]

    C. Wang, O. Tsepa, J. Ma, B. Wang, Graph-mamba: Towards long-range graph sequence modeling with selective state spaces, arXiv preprint arXiv:2402.00789 (2024)

    arXiv 2024

  7. [4]

    R. Liu, P. Xing, Z. Deng, A. Li, C. Guan, H. Yu, Federated graph neural networks: Overview, techniques, and challenges, IEEE Transactions on Neural Networks and Learning Systems (2024)

    2024

  8. [5]

    Khemani, S

    B. Khemani, S. Patil, K. Kotecha, S. Tanwar, A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions, Journal of Big Data 11 (1) (2024) 18

    2024

Show all 104 references
  1. [6]

    Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, S. Y. Philip, A comprehensive survey on graph neural networks, IEEE transactions on neural networks and learning systems 32 (1) (2020) 4–24

    2020

  2. [7]

    B. N. Patro, V. S. Agneeswaran, Mamba-360: Survey of state space models as transformer alternative for long sequence modelling: Methods, applications, and challenges, arXiv preprint arXiv:2404.16112 (2024)

    2024 arXiv

  3. [8]

    H. Qu, L. Ning, R. An, W. Fan, T. Derr, X. Xu, Q. Li, A survey of mamba, arXiv preprint arXiv:2408.01129 (2024)

    2024 arXiv

  4. [9]

    X. Wang, S. Wang, Y. Ding, Y. Li, W. Wu, Y. Rong, W. Kong, J. Huang, S. Li, H. Yang, et al., State space model for new-generation network alternative to transformers: A survey, arXiv preprint arXiv:2404.09516 (2024)

    2024 arXiv

  5. [10]

    M. M. Rahman, A. A. Tutul, A. Nath, L. Laishram, S. K. Jung, T. Hammond, Mamba in vision: A comprehensive survey of techniques and applications, arXiv preprint arXiv:2410.03105 (2024)

    2024 arXiv

  6. [11]

    X. Liu, C. Zhang, L. Zhang, Vision mamba: A comprehensive survey and taxonomy, arXiv preprint arXiv:2405.04404 (2024)

    2024 arXiv

  7. [12]

    T. N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, arXiv preprint arXiv:1609.02907 (2016)

    2016 arXiv

  8. [14]

    Boulila, H

    W. Boulila, H. Ghandorh, S. Masood, A. Alzahem, A. Koubaa, F. Ahmed, Z. Khan, J. Ahmad, A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing, Heliyon 10 (8) (2024)

    2024

  9. [15]

    A. Gu, T. Dao, Mamba: Linear-time sequence modeling with selective state spaces, arXiv preprint arXiv:2312.00752 (2023)

    2023 arXiv

  10. [16]

    J. D. Hamilton, State-space models, Handbook of econometrics 4 (1994) 3039–3080

    1994

  11. [17]

    A. Gu, K. Goel, C. Ré, Efficiently modeling long sequences with structured state spaces, arXiv preprint arXiv:2111.00396 (2021)

    2021 arXiv

  12. [18]

    J. T. Smith, A. Warrington, S. W. Linderman, Simplified state space layers for sequence modeling, arXiv preprint arXiv:2208.04933 (2022)

    2022 arXiv

  13. [19]

    Liang, L

    K. Liang, L. Meng, M. Liu, Y. Liu, W. Tu, S. Wang, S. Zhou, X. Liu, F. Sun, K. He, A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal, IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

    2024

  14. [20]

    L. Li, H. Wang, W. Zhang, A. Coster, Stg-mamba: Spatial-temporal graph learning via selective state space model, arXiv preprint arXiv:2403.12418 (2024)

    2024 arXiv

  15. [21]

    D. Li, S. Tan, Y. Zhang, M. Jin, S. Pan, M. Okumura, R. Jiang, Dyg-mamba: Continuous state space modeling on dynamic graphs, arXiv preprint arXiv:2408.06966 (2024)

    2024

  16. [22]

    Z. Ding, Y. Li, Y. He, A. Norelli, J. Wu, V. Tresp, Y. Ma, M. Bronstein, Dygmamba: Efficiently modeling long-term temporal dependency on continuous-time dynamic graphs with state space models, arXiv preprint arXiv:2408.04713 (2024)

    2024 arXiv

  17. [23]

    R. Bing, G. Yuan, M. Zhu, F. Meng, H. Ma, S. Qiao, Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications, Artificial Intelligence Review 56 (8) (2023) 8003–8042

    2023

  18. [24]

    Z. Pan, Y. Jeong, X. Liu, H. Liu, Hetegraph-mamba: Heterogeneous graph learning via selective state space model, arXiv preprint arXiv:2405.13915 (2024)

    2024 arXiv

  19. [25]

    J. Choi, H. Kim, M. An, J. J. Whang, Spot-mamba: Learning long-range dependency on spatio-temporal graphs with selective state spaces, arXiv preprint arXiv:2406.11244 (2024)

    2024 arXiv

  20. [26]

    K. Han, C. Shi, Z. Wang, W. Liu, Z. Li, Z. Wang, L. Lei, R. Dai, M. Wang, Z. Zhang, et al., Innovative mamba and graph transformer framework for superior protein-ligand affinity prediction, Microchemical Journal 206 (2024) 111444

    2024

  21. [27]

    Mehrabian, E

    A. Mehrabian, E. Hoseinzade, M. Mazloum, X. Chen, Mamba meets financial markets: A graph-mamba approach for stock price prediction, arXiv preprint arXiv:2410.03707 (2024)

    2024 arXiv

  22. [28]

    Zhang, Y

    C. Zhang, Y. He, W. Li, J. Zhang, X. Cui, Gm-unet: Graph mamba unet for medical image segmentation, in: 2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), IEEE, 2024, pp. 1373–1379

    2024

  23. [29]

    L. Zhou, Z. Yi, K. Zhou, X. Xu, Efficient and gender-adaptive graph vision mamba for pediatric bone age assessment, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2024, pp. 230–239. , Vol. 1, No. 1, Article . Publication d...

    2024

  24. [30]

    H. Dong, A. Chharia, W. Gou, F. V. Carrasco, F. De la Torre, Hamba: Single-view 3d hand reconstruction with graph-guided bi-scanning mamba, arXiv preprint arXiv:2407.09646 (2024)

    2024

  25. [31]

    H. Xu, T. Shi, W. Liu, H. Zhao, G. Kong, Identifying subphenotypes for sepsis with acute kidney injury via multimodal graph state space models, in: Artificial Intelligence and Data Science for Healthcare: Bridging Data-Centric AI and People-Centric Healthcare, 2024

    2024

  26. [32]

    Behrouz, F

    A. Behrouz, F. Hashemi, Graph mamba: Towards learning on graphs with state space models, in: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024, pp. 119–130

    2024

  27. [33]

    Behrouz, F

    A. Behrouz, F. Hashemi, Brain-mamba: Encoding brain activity via selective state space models, in: Conference on Health, Inference, and Learning, PMLR, 2024, pp. 233–250

    2024

  28. [34]

    A. Yang, M. Li, Y. Ding, L. Fang, Y. Cai, Y. He, Graphmamba: An efficient graph structure learning vision mamba for hyperspectral image classification, arXiv preprint arXiv:2407.08255 (2024)

    2024 arXiv

  29. [35]

    S. B. Atitallah, M. Driss, W. Boulila, H. B. Ghézala, Leveraging deep learning and iot big data analytics to support the smart cities development: Review and future directions, Computer Science Review 38 (2020) 100303

    2020

  30. [36]

    L. Qiao, L. Zhang, S. Chen, D. Shen, Data-driven graph construction and graph learning: A review, Neurocomputing 312 (2018) 336–351

    2018

  31. [37]

    Khoshraftar, A

    S. Khoshraftar, A. An, A survey on graph representation learning methods, ACM Transactions on Intelligent Systems and Technology 15 (1) (2024) 1–55

    2024

  32. [38]

    Montagna, S

    M. Montagna, S. Scardapane, L. Telyatnikov, Topological deep learning with state-space models: A mamba approach for simplicial complexes, arXiv preprint arXiv:2409.12033 (2024)

    2024 arXiv

  33. [39]

    X. Zhu, A. B. Goldberg, Introduction to semi-supervised learning, Springer Nature, 2022

    2022

  34. [40]

    Z. Song, X. Yang, Z. Xu, I. King, Graph-based semi-supervised learning: A comprehensive review, IEEE Transactions on Neural Networks and Learning Systems 34 (11) (2022) 8174–8194

    2022

  35. [41]

    G. Zhao, T. Wang, Y. Jin, C. Lang, Y. Li, H. Ling, Grassnet: State space model meets graph neural network, arXiv preprint arXiv:2408.08583 (2024)

    2024 arXiv

  36. [42]

    L. Wu, H. Lin, C. Tan, Z. Gao, S. Z. Li, Self-supervised learning on graphs: Contrastive, generative, or predictive, IEEE Transactions on Knowledge and Data Engineering 35 (4) (2021) 4216–4235

    2021

  37. [43]

    Y. Xie, Z. Xu, J. Zhang, Z. Wang, S. Ji, Self-supervised learning of graph neural networks: A unified review, IEEE transactions on pattern analysis and machine intelligence 45 (2) (2022) 2412–2429

    2022

  38. [44]

    Ben Atitallah, M

    S. Ben Atitallah, M. Driss, W. Boulila, A. Koubaa, Strengthening network intrusion detection in iot environments with self-supervised learning and few shot learning, in: International Conference on Computational Collective Intelligence, Springer, 2024, pp. 83–96

    2024

  39. [45]

    Benjdira, A

    B. Benjdira, A. M. Ali, A. Koubaa, A. Ammar, W. Boulila, Dm–ahr: A self-supervised conditional diffusion model for ai-generated hairless imaging for enhanced skin diagnosis applications, Cancers 16 (17) (2024) 2947

    2024

  40. [46]

    Ding, K.-D

    R. Ding, K.-D. Luong, E. Rodriguez, A. C. A. L. da Silva, W. Hsu, Combining graph neural network and mamba to capture local and global tissue spatial relationships in whole slide images, arXiv preprint arXiv:2406.04377 (2024)

    2024 arXiv

  41. [48]

    W. Ju, Z. Fang, Y. Gu, Z. Liu, Q. Long, Z. Qiao, Y. Qin, J. Shen, F. Sun, Z. Xiao, et al., A comprehensive survey on deep graph representation learning, Neural Networks (2024) 106207

    2024

  42. [49]

    J. Li, R. Wu, X. Jin, B. Ma, L. Chen, Z. Zheng, State space models on temporal graphs: A first-principles study, arXiv preprint arXiv:2406.00943 (2024)

    2024 arXiv

  43. [50]

    Huang, S

    Y. Huang, S. Miao, P. Li, What can we learn from state space models for machine learning on graphs?, arXiv preprint arXiv:2406.05815 (2024)

    2024 arXiv

  44. [51]

    D. A. Tedjopurnomo, Z. Bao, B. Zheng, F. M. Choudhury, A. K. Qin, A survey on modern deep neural network for traffic prediction: Trends, methods and challenges, IEEE Transactions on Knowledge and Data Engineering 34 (4) (2020) 1544–1561

    2020

  45. [52]

    Shaygan, C

    M. Shaygan, C. Meese, W. Li, X. G. Zhao, M. Nejad, Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities, Transportation research part C: emerging technologies 145 (2022) 103921

    2022

  46. [53]

    Zhang, Y

    Y. Zhang, Y. Zou, H. Zhang, Z. Liu, H. Li, L. Cheng, Enhanced prediction of multi-agent trajectories via control inference and state-space dynamics, arXiv preprint arXiv:2408.12609 (2024)

    2024 arXiv

  47. [54]

    H. Zhou, D. Yao, B. Chen, K. Yu, X. Wu, Graph convolution network based state space model for wireless traffic prediction, in: 2024 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, 2024, pp. 1–6

    2024

  48. [55]

    M. M. Li, K. Huang, M. Zitnik, Graph representation learning in biomedicine and healthcare, Nature Biomedical Engineering 6 (12) (2022) 1353–1369

    2022

  49. [56]

    S. G. Paul, A. Saha, M. Z. Hasan, S. R. H. Noori, A. Moustafa, A systematic review of graph neural network in healthcare-based applications: Recent advances, trends, and future directions, IEEE Access (2024). , Vol. 1, No. 1, Article . Publication date: December 2024. 34 Ben A...

    2024

  50. [57]

    Aghaee, S

    M. Aghaee, S. Krau, M. Tamer, H. Budman, Graph neural network representation of state space models of metabolic pathways, IFAC-PapersOnLine 58 (14) (2024) 464–469

    2024

  51. [59]

    L. Jiao, J. Chen, F. Liu, S. Yang, C. You, X. Liu, L. Li, B. Hou, Graph representation learning meets computer vision: A survey, IEEE Transactions on Artificial Intelligence 4 (1) (2022) 2–22

    2022

  52. [60]

    Szeliski, Computer vision: algorithms and applications, Springer Nature, 2022

    R. Szeliski, Computer vision: algorithms and applications, Springer Nature, 2022

    2022

  53. [61]

    C. Chen, Y. Wu, Q. Dai, H.-Y. Zhou, M. Xu, S. Yang, X. Han, Y. Yu, A survey on graph neural networks and graph transformers in computer vision: A task-oriented perspective, IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

    2024

  54. [62]

    M. K. Khlifi, W. Boulila, I. R. Farah, Graph-based deep learning techniques for remote sensing applications: Techniques, taxonomy, and applications—a comprehensive review, Computer Science Review 50 (2023) 100596

    2023

  55. [63]

    Sonkavde, D

    G. Sonkavde, D. S. Dharrao, A. M. Bongale, S. T. Deokate, D. Doreswamy, S. K. Bhat, Forecasting stock market prices using machine learning and deep learning models: A systematic review, performance analysis and discussion of implications, International Journal of Financial Stu...

    2023

  56. [64]

    M. N. Ashtiani, B. Raahemi, News-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review, Expert Systems with Applications 217 (2023) 119509

    2023

  57. [65]

    Lawan, J

    A. Lawan, J. Pu, H. Yunusa, A. Umar, M. Lawan, Mambaforgcn: Enhancing long-range dependency with state space model and kolmogorov-arnold networks for aspect-based sentiment analysis, arXiv preprint arXiv:2407.10347 (2024)

    2024 arXiv

  58. [66]

    Z. Liu, Y. Wang, S. Vaidya, F. Ruehle, J. Halverson, M. Soljačić, T. Y. Hou, M. Tegmark, Kan: Kolmogorov-arnold networks, arXiv preprint arXiv:2404.19756 (2024)

    2024 arXiv

  59. [67]

    Romero, D

    J. Romero, D. Tzionas, M. J. Black, Embodied hands: Modeling and capturing hands and bodies together, arXiv preprint arXiv:2201.02610 (2022)

    2022 arXiv

  60. [68]

    H. Liu, Z. Dong, R. Jiang, J. Deng, J. Deng, Q. Chen, X. Song, Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting, in: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, ACM, 2023, pp. 4125–4129

    2023

  61. [69]

    Sutskever, O

    I. Sutskever, O. Vinyals, Q. V. Le, Sequence to sequence learning with neural networks, arXiv preprint arXiv:1409.3215 (2014)

    2014 arXiv

  62. [70]

    L. Liu, J. Chen, H. Wu, J. Zhen, G. Li, L. Lin, Physical-virtual collaboration modeling for intra-and inter-station metro ridership prediction, IEEE Transactions on Intelligent Transportation Systems 23 (4) (2020) 3377–3391

    2020

  63. [71]

    J. Tian, Y. Liang, R. Xu, P. Chen, C. Guo, A. Zhou, L. Pan, Z. Rao, B. Yang, Air quality prediction with physics-informed dual neural odes in open systems, arXiv preprint arXiv:2410.19892 (2024)

    2024 arXiv

  64. [72]

    B. Yu, H. Yin, Z. Zhu, Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting, arXiv preprint arXiv:1709.04875 (2017)

    2017 arXiv

  65. [73]

    Audibert, P

    J. Audibert, P. Michiardi, F. Guyard, S. Marti, M. A. Zuluaga, Usad: Unsupervised anomaly detection on multivariate time series, in: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020, pp. 3395–3404

    2020

  66. [74]

    X. He, B. Hooi, T. Laurent, A. Perold, Y. LeCun, X. Bresson, A generalization of vit/mlp-mixer to graphs, in: International conference on machine learning, PMLR, 2023, pp. 12724–12745

    2023

  67. [75]

    W. Cong, S. Zhang, J. Kang, B. Yuan, H. Wu, X. Zhou, H. Tong, M. Mahdavi, Do we really need complicated model architectures for temporal networks?, arXiv preprint arXiv:2302.11636 (2023)

    2023 arXiv

  68. [76]

    Kawahara, C

    J. Kawahara, C. J. Brown, S. P. Miller, B. G. Booth, V. Chau, R. E. Grunau, J. G. Zwicker, G. Hamarneh, Brainnetcnn: Convolutional neural networks for brain networks; towards predicting neurodevelopment, NeuroImage 146 (2017) 1038–1049

    2017

  69. [77]

    X. Li, Y. Zhou, N. Dvornek, M. Zhang, S. Gao, J. Zhuang, D. Scheinost, L. H. Staib, P. Ventola, J. S. Duncan, Braingnn: Interpretable brain graph neural network for fmri analysis, Medical Image Analysis 74 (2021) 102233

    2021

  70. [78]

    X. Kan, H. Cui, J. Lukemire, Y. Guo, C. Yang, Fbnetgen: Task-aware gnn-based fmri analysis via functional brain network generation, in: International Conference on Medical Imaging with Deep Learning, PMLR, 2022, pp. 618–637

    2022

  71. [79]

    Behrouz, M

    A. Behrouz, M. Seltzer, Admire++: Explainable anomaly detection in the human brain via inductive learning on temporal multiplex networks, in: ICML 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH), 2023

    2023

  72. [80]

    Y. Yang, H. Cui, C. Yang, Ptgb: Pre-train graph neural networks for brain network analysis, arXiv preprint arXiv:2305.14376 (2023)

    2023 arXiv

  73. [81]

    X. Kan, W. Dai, H. Cui, Z. Zhang, Y. Guo, C. Yang, Brain network transformer, Advances in Neural Information Processing Systems 35 (2022) 25586–25599

    2022

  74. [82]

    Behrouz, F

    A. Behrouz, F. Hashemi, Learning temporal higher-order patterns to detect anomalous brain activity, in: Machine Learning for Health (ML4H), PMLR, 2023, pp. 39–51. , Vol. 1, No. 1, Article . Publication date: December 2024. Exploring Graph Mamba: A Comprehensive Survey on State...

    2023

  75. [83]

    S. Mei, X. Li, X. Liu, H. Cai, Q. Du, Hyperspectral image classification using attention-based bidirectional long short-term memory network, IEEE Transactions on Geoscience and Remote Sensing 60 (2021) 1–12

    2021

  76. [84]

    A. B. Hamida, A. Benoit, P. Lambert, C. B. Amar, 3-d deep learning approach for remote sensing image classification, IEEE Transactions on Geoscience and Remote Sensing 56 (8) (2018) 4420–4434

    2018

  77. [85]

    S. Mei, C. Song, M. Ma, F. Xu, Hyperspectral image classification using group-aware hierarchical transformer, IEEE Transactions on Geoscience and Remote Sensing 60 (2022) 1–14

    2022

  78. [86]

    Moghar, M

    A. Moghar, M. Hamiche, Stock market prediction using lstm recurrent neural network, Procedia computer science 170 (2020) 1168–1173

    2020

  79. [87]

    C. Wang, Y. Chen, S. Zhang, Q. Zhang, Stock market index prediction using deep transformer model, Expert Systems with Applications 208 (2022) 118128

    2022

  80. [88]

    K. Yi, Q. Zhang, W. Fan, H. He, L. Hu, P. Wang, N. An, L. Cao, Z. Niu, Fouriergnn: Rethinking multivariate time series forecasting from a pure graph perspective, Advances in Neural Information Processing Systems 36 (2024)

    2024

  81. [89]

    H. T. Phan, N. T. Nguyen, D. Hwang, Aspect-level sentiment analysis using cnn over bert-gcn, IEEE Access 10 (2022) 110402–110409

    2022

  82. [90]

    Liu, et al., Enhancing aspect-based sentiment analysis using a dual-gated graph convolutional network via contextual affective knowledge, Neurocomputing 553 (2023) 126526

    H. Liu, et al., Enhancing aspect-based sentiment analysis using a dual-gated graph convolutional network via contextual affective knowledge, Neurocomputing 553 (2023) 126526

    2023

  83. [91]

    H. Tang, D. Ji, C. Li, Q. Zhou, Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 2020, pp. 657...

    2020 doi

  84. [92]

    Z. Liu, K. Shi, K. Zhang, W. Ou, L. Wang, Discriminative sparse embedding based on adaptive graph for dimension reduction, Engineering Applications of Artificial Intelligence 94 (2020) 103758

    2020

  85. [93]

    J. Hu, P. Lin, H. Zhang, Z. Lan, W. Chen, K. Xie, S. Chen, H. Wang, S. Chang, A dynamic pruning method on multiple sparse structures in deep neural networks, IEEE Access 11 (2023) 38448–38457

    2023

  86. [94]

    Gurevin, M

    D. Gurevin, M. Shan, S. Huang, M. A. Hasan, C. Ding, O. Khan, Prunegnn: Algorithm-architecture pruning framework for graph neural network acceleration, in: 2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA), IEEE, 2024, pp. 108–123

    2024

  87. [95]

    A. Feng, C. You, S. Wang, L. Tassiulas, Kergnns: Interpretable graph neural networks with graph kernels, in: Proceedings of the AAAI conference on artificial intelligence, Vol. 36, 2022, pp. 6614–6622

    2022

  88. [96]

    Chaddad, Q

    A. Chaddad, Q. Lu, J. Li, Y. Katib, R. Kateb, C. Tanougast, A. Bouridane, A. Abdulkadir, Explainable, domain-adaptive, and federated artificial intelligence in medicine, IEEE/CAA Journal of Automatica Sinica 10 (4) (2023) 859–876

    2023

  89. [97]

    Spinelli, S

    I. Spinelli, S. Scardapane, A. Uncini, Adaptive propagation graph convolutional network, IEEE Transactions on Neural Networks and Learning Systems 32 (10) (2020) 4755–4760

    2020

  90. [98]

    Z. Yang, T. Huang, M. Ding, Y. Dong, R. Ying, Y. Cen, Y. Geng, J. Tang, Batchsampler: Sampling mini-batches for contrastive learning in vision, language, and graphs, in: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023, pp. 3057–3069

    2023

  91. [99]

    Y. Liu, M. Jin, S. Pan, C. Zhou, Y. Zheng, F. Xia, S. Y. Philip, Graph self-supervised learning: A survey, IEEE transactions on knowledge and data engineering 35 (6) (2022) 5879–5900

    2022

  92. [100]

    Chong, Y

    Y. Chong, Y. Ding, Q. Yan, S. Pan, Graph-based semi-supervised learning: A review, Neurocomputing 408 (2020) 216–230

    2020

  93. [101]

    Z. Feng, R. Wang, T. Wang, M. Song, S. Wu, S. He, A comprehensive survey of dynamic graph neural networks: Models, frameworks, benchmarks, experiments and challenges, arXiv preprint arXiv:2405.00476 (2024)

    2024 arXiv

  94. [102]

    X. Zou, Y. Yan, X. Hao, Y. Hu, H. Wen, E. Liu, J. Zhang, et al., Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook, Information Fusion 113 (2025) 102606

    2025

  95. [103]

    Z. Chen, Y. Zhang, Y. Fang, Y. Geng, L. Guo, X. Chen, Q. Li, et al., Knowledge graphs meet multi-modal learning: A comprehensive survey, arXiv preprint arXiv:2402.05391 (2024)

    2024 arXiv

  96. [104]

    B. Shi, Y. Wang, F. Guo, B. Xu, H. Shen, X. Cheng, Graph domain adaptation: Challenges, progress and prospects, arXiv preprint arXiv:2402.00904 (2024). , Vol. 1, No. 1, Article . Publication date: December 2024

    2024 arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.