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An Efficient and Scalable Graph Condensation with Structure-Preserving

T0 review · 2 major / 0 minor · reviewed 2026-06-29 · grok-4.3

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Pith's one-line read SP-ESGC produces accurate synthetic graphs from large originals by decoupling node condensation from structure generation.

desk verdict The decoupled pipeline using heat kernel, hybrid clustering, and a pre-trained edge predictor is the main idea, but its success depends on whether that predictor actually transfers structure reliably without joint optimization. read the letter →

arxiv 2605.31016 v1 pith:XAN3AKME submitted 2026-05-29 cs.LG

Yulin Hu , Fuyan Ou , Ye Yuan This is my paper · ORCID
classification cs.LG
keywords graphcondensationneuralnetworksstructurepreservationdecoupledoptimizationheatkernelpropagationsyntheticgraphsefficientmethodsscalable
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

Graph condensation compresses big graphs into small synthetic ones so that GNNs can train and run with far less memory and time. Prior methods jointly optimize nodes and edges in one slow loop and often fail when the downstream GNN changes. SP-ESGC first diffuses node features with a heat kernel, clusters them into class centroids, then feeds the condensed nodes to a separate pre-trained edge predictor that copies structural patterns from the original graph. Experiments on real datasets show the resulting synthetic graphs match the accuracy of coupled baselines while running much faster and working across different GNN architectures.

What carries the argument

The decoupled design that separates node condensation from graph structure generation via a pre-trained edge predictor.

What would settle it

On a standard benchmark dataset, measure GNN test accuracy on SP-ESGC synthetic graphs versus coupled-optimization baselines; a large consistent drop in accuracy would falsify the claim of precise condensation.

Watch

Extended reading notes

Core claim

By separating node condensation (via heat-kernel propagation and hybrid clustering) from structure generation (via a pre-trained edge predictor), SP-ESGC achieves precise graph condensation at high computational efficiency and generalizes across diverse GNN architectures without requiring joint optimization.

Load-bearing premise

The pre-trained edge predictor can reliably infer transferable structural patterns from the original graph to produce accurate synthetic graphs without coupled optimization.

Editorial extensions

If this is right

  • Synthetic graphs can be generated in a single forward pass rather than iterative joint optimization.
  • The same condensed nodes can be paired with structures inferred by different predictors for different tasks.
  • Condensation time scales independently of the number of GNN architectures that will later use the output.
  • The method applies directly to large real-world graphs without prohibitive memory growth during condensation.

Reading between the lines

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

  • Once the edge predictor is trained, condensation of new graphs in the same domain could reuse it without retraining.
  • The separation might let practitioners swap in stronger node-embedding methods while keeping the same structure predictor.
  • Similar decoupling could be tested on temporal or heterogeneous graphs if an appropriate pre-trained predictor is supplied.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 0 minor

Summary. The manuscript proposes SP-ESGC for graph condensation, using a decoupled pipeline: heat-kernel diffusion to generate node representations via spectral theory, hybrid clustering to extract intra-class centroids, and a separately pre-trained edge predictor to infer transferable structural patterns for the synthetic graph. The central claims are that this yields precise condensation (retaining GNN performance) with significantly higher computational efficiency than coupled-optimization baselines, plus strong generalization across GNN architectures, as shown by experiments on real-world datasets.

Significance. A working decoupled GC method could be significant for scaling GNNs to large graphs by avoiding the computational cost of joint node-structure optimization. The heat-kernel plus hybrid-clustering node step and the pre-trained predictor for structure are technically interesting if they deliver the claimed preservation of performance. However, the significance is currently limited because the provided text supplies no quantitative results, baselines, runtime numbers, or error analysis, and the key transferability assumption on the edge predictor lacks supporting derivation or bounds.

major comments (2)
  1. [Abstract] Abstract and method overview: the claims of 'precise GC with significantly high computational efficiency' and 'generalizes well across diverse GNN architectures' are asserted without any reported accuracy numbers, runtime comparisons, baseline methods, or statistical analysis, so the central empirical claims cannot be evaluated from the supplied text.
  2. [Method (edge predictor)] Method description of the edge-predictor stage: the pipeline decouples node condensation from edge generation and relies on a pre-trained predictor to reconstruct edges that preserve the properties needed for downstream GNN accuracy. No derivation, spectral bound, or topological guarantee is supplied showing that the predictor's output from the condensed node set will retain the necessary structure; the claim therefore rests entirely on an unverified empirical assertion of transferability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback. We address the major comments point by point below, clarifying the role of the abstract and the empirical basis of the edge predictor while noting where the manuscript can be strengthened.

read point-by-point responses

  1. Referee: [Abstract] Abstract and method overview: the claims of 'precise GC with significantly high computational efficiency' and 'generalizes well across diverse GNN architectures' are asserted without any reported accuracy numbers, runtime comparisons, baseline methods, or statistical analysis, so the central empirical claims cannot be evaluated from the supplied text.

    Authors: The abstract serves as a high-level summary of the method and its intended benefits. The full manuscript includes a dedicated Experiments section reporting accuracy metrics, runtime comparisons against coupled baselines, results on multiple real-world datasets, and cross-GNN generalization tests with statistical details. If the version provided to the referee omitted these sections or tables, we will ensure the complete manuscript is supplied. We will revise the abstract to include one or two concrete performance highlights for better self-containment. revision: partial

  2. Referee: [Method (edge predictor)] Method description of the edge-predictor stage: the pipeline decouples node condensation from edge generation and relies on a pre-trained predictor to reconstruct edges that preserve the properties needed for downstream GNN accuracy. No derivation, spectral bound, or topological guarantee is supplied showing that the predictor's output from the condensed node set will retain the necessary structure; the claim therefore rests entirely on an unverified empirical assertion of transferability.

    Authors: The edge predictor is pre-trained on the original graph to capture transferable structural patterns and then applied to the condensed node set. The manuscript supports the approach through empirical results demonstrating that the resulting synthetic graphs preserve downstream GNN accuracy across architectures. We agree that no formal derivation, spectral bound, or topological guarantee is provided; the transferability claim is validated empirically rather than theoretically. This is a genuine limitation of the current work. We will add a paragraph in the method section discussing the empirical evidence, the decoupling assumptions, and the lack of theoretical bounds as an avenue for future analysis. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; derivation is self-contained

full rationale

The paper presents a decoupled three-stage pipeline (heat-kernel diffusion for node features, hybrid clustering for condensed nodes, and a separately pre-trained edge predictor for structure) whose outputs are not shown by any equation or self-citation to be definitionally equivalent to its inputs. No fitted parameter is relabeled as a prediction, no uniqueness theorem is imported from the authors' prior work, and the central efficiency and generalization claims rest on external empirical results rather than internal reductions. This is the normal non-circular case for a methods paper whose load-bearing step is an empirical assertion about the edge predictor's transferability.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

Abstract-only review limits visibility into parameters or assumptions; no explicit free parameters or invented entities named.

assumptions (2)
  • domain assumption Heat kernel feature propagation via spectral graph theory produces discriminative node representations suitable for clustering.
    Invoked as the first step of the method.
  • domain assumption A pre-trained edge predictor can extract transferable structural patterns independent of the specific GNN used downstream.
    Central to the decoupled structure generation step.

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Cite this review

Pith. "Pith review of An Efficient and Scalable Graph Condensation with Structure-Preserving." pith.science (2026). https://pith.science/paper/XAN3AKME

@misc{pith2026260531016,
  author       = {Pith},
  title        = {Pith review of: An Efficient and Scalable Graph Condensation with Structure-Preserving},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XAN3AKME}},
  note         = {Machine review of arXiv:2605.31016}
}
read the original abstract

Graph condensation (GC) is pivotal for enabling Graph Neural Networks (GNNs) deployment in resource-constrained scenarios by compressing large-scale graphs into compact synthetic counterparts. Existing GC methods commonly suffer from computational inefficiency due to coupled optimization as well as encountering poor generalization across GNN architectures. To address these challenges, this study proposes an Efficient and Scalable Graph Condensation with Structure-Preserving (SP-ESGC), which possesses a decoupled design that separates node condensation from graph structure generation. Specifically, it first employs heat kernel feature propagation to generate node representation via spectral graph theory-inspired diffusion. Further, a novel hybrid clustering strategy is designed to extracts discriminative intra-class centroids from the node representation. Finally, a pre-trained edge predictor infers transferable structural patterns from the original graph, ensuring accurate synthetic graph generation. Extensive experiments on real-world graph datasets demonstrate that the proposed SP-ESGC implementes a precise GC with significantly high computational efficiency. Moreover, SP-ESGC also generalizes well across diverse GNN architectures.

Figures

Figures reproduced from arXiv: 2605.31016 by the authors.

Figure 1
Figure 1. A schematic diagram of the proposed SP-ESGC framework. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The generalization capabilities of SP-ESGC on five datasets. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

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Reference graph

Works this paper leans on

85 extracted references · 11 canonical work pages

  1. [1]

    Influence maximization in real-world closed social networks,

    S. Huang, W. Lin, Z. Bao, and J. Sun, “Influence maximization in real-world closed social networks,”arXiv preprint arXiv:2209.10286, 2022

    work page arXiv 2022

  2. [2]

    Do transformers really perform badly for graph representation?

    C. Ying, T. Cai, S. Luo, S. Zheng, G. Ke, D. He, Y . Shen, and T.-Y . Liu, “Do transformers really perform badly for graph representation?”Advances in neural information processing systems, vol. 34, pp. 28 877–28 888, 2021

    2021

  3. [3]

    Graph neural networks for social recommendation,

    W. Fan, Y . Ma, Q. Li, Y . He, E. Zhao, J. Tang, and D. Yin, “Graph neural networks for social recommendation,” inThe world wide web conference, 2019, pp. 417–426

    2019

  4. [4]

    Graph neural networks: Methods, applications, and opportunities,

    L. Waikhom and R. Patgiri, “Graph neural networks: Methods, applications, and opportunities,”arXiv preprint arXiv:2108.10733, 2021

    work page arXiv 2021

  5. [5]

    Nas-bench-graph: Benchmarking graph neural architecture search,

    Y . Qin, Z. Zhang, X. Wang, Z. Zhang, and W. Zhu, “Nas-bench-graph: Benchmarking graph neural architecture search,” Advances in neural information processing systems, vol. 35, pp. 54–69, 2022

    2022

  6. [6]

    Remember the past: Distilling datasets into addressable memories for neural networks,

    Z. Deng and O. Russakovsky, “Remember the past: Distilling datasets into addressable memories for neural networks,” Advances in Neural Information Processing Systems, vol. 35, pp. 34 391–34 404, 2022

    2022

  7. [7]

    Dataset Distillation

    T. Wang, J.-Y . Zhu, A. Torralba, and A. A. Efros, “Dataset distillation,”arXiv preprint arXiv:1811.10959, 2018

    work page Pith review arXiv 2018

  8. [8]

    Does graph distillation see like vision dataset counterpart?

    B. Yang, K. Wang, Q. Sun, C. Ji, X. Fu, H. Tang, Y . You, and J. Li, “Does graph distillation see like vision dataset counterpart?”Advances in Neural Information Processing Systems, vol. 36, pp. 53 201–53 226, 2023

    2023

Show all 85 references
  1. [9]

    Dataset condensation with differentiable siamese augmentation,

    B. Zhao and H. Bilen, “Dataset condensation with differentiable siamese augmentation,” inInternational Conference on Machine Learning. PMLR, 2021, pp. 12 674–12 685

    2021

  2. [10]

    Dataset condensation with gradient matching,

    B. Zhao, K. R. Mopuri, and H. Bilen, “Dataset condensation with gradient matching,”arXiv preprint arXiv:2006.05929, 2020

    2006

  3. [11]

    Dataset condensation with distribution matching,

    B. Zhao and H. Bilen, “Dataset condensation with distribution matching,” inProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 6514–6523

    2023

  4. [12]

    Dataset meta-learning from kernel ridge-regression,

    T. Nguyen, Z. Chen, and J. Lee, “Dataset meta-learning from kernel ridge-regression,”arXiv preprint arXiv:2011.00050, 2020

    2011

  5. [13]

    Cafe: Learning to condense dataset by aligning features,

    K. Wang, B. Zhao, X. Peng, Z. Zhu, S. Yang, S. Wang, G. Huang, H. Bilen, X. Wang, and Y . You, “Cafe: Learning to condense dataset by aligning features,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12 196–12 205

    2022

  6. [14]

    Graph condensation for graph neural networks,

    W. Jin, L. Zhao, S. Zhang, Y . Liu, J. Tang, and N. Shah, “Graph condensation for graph neural networks,”arXiv preprint arXiv:2110.07580, 2021

    2021

  7. [15]

    Condensing graphs via one-step gradient matching,

    W. Jin, X. Tang, H. Jiang, Z. Li, D. Zhang, J. Tang, and B. Yin, “Condensing graphs via one-step gradient matching,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 720–730

    2022

  8. [16]

    Graph condensation via receptive field distribution matching,

    M. Liu, S. Li, X. Chen, and L. Song, “Graph condensation via receptive field distribution matching,”arXiv preprint arXiv:2206.13697, 2022

    2022

  9. [17]

    Structure-free graph condensation: From large-scale graphs to condensed graph-free data,

    X. Zheng, M. Zhang, C. Chen, Q. V . H. Nguyen, X. Zhu, and S. Pan, “Structure-free graph condensation: From large-scale graphs to condensed graph-free data,”Advances in Neural Information Processing Systems, vol. 36, pp. 6026–6047, 2023

    2023

  10. [18]

    Navigating complexity: Toward lossless graph condensation via expanding window matching,

    Y . Zhang, T. Zhang, K. Wang, Z. Guo, Y . Liang, X. Bresson, W. Jin, and Y . You, “Navigating complexity: Toward lossless graph condensation via expanding window matching,”arXiv preprint arXiv:2402.05011, 2024

    2024

  11. [19]

    Fast graph condensation with structure-based neural tangent kernel,

    L. Wang, W. Fan, J. Li, Y . Ma, and Q. Li, “Fast graph condensation with structure-based neural tangent kernel,” in Proceedings of the ACM Web Conference 2024, 2024, pp. 4439–4448

    2024

  12. [20]

    Kernel ridge regression-based graph dataset distillation,

    Z. Xu, Y . Chen, M. Pan, H. Chen, M. Das, H. Yang, and H. Tong, “Kernel ridge regression-based graph dataset distillation,” inProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023, pp. 2850–2861

    2023

  13. [21]

    Simple graph condensation,

    Z. Xiao, Y . Wang, S. Liu, H. Wang, M. Song, and T. Zheng, “Simple graph condensation,” inJoint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 2024, pp. 53–71

    2024

  14. [22]

    Rethinking and accelerating graph condensation: A training-free approach with class partition,

    X. Gao, G. Ye, T. Chen, W. Zhang, J. Yu, and H. Yin, “Rethinking and accelerating graph condensation: A training-free approach with class partition,” inProceedings of the ACM on Web Conference 2025, 2025, pp. 4359–4373

    2025

  15. [23]

    Graph spectral image smoothing using the heat kernel,

    F. Zhang and E. R. Hancock, “Graph spectral image smoothing using the heat kernel,”Pattern recognition, vol. 41, no. 11, pp. 3328–3342, 2008

    2008

  16. [24]

    Adaptive diffusion in graph neural networks,

    J. Zhao, Y . Dong, M. Ding, E. Kharlamov, and J. Tang, “Adaptive diffusion in graph neural networks,”Advances in neural information processing systems, vol. 34, pp. 23 321–23 333, 2021

    2021

  17. [25]

    Distributed implementation of heat kernel smoothing for graph signal denoising,

    C.-C. Tseng and S.-L. Lee, “Distributed implementation of heat kernel smoothing for graph signal denoising,” in2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2022, pp. 1–4

    2022

  18. [26]

    Fast approximate truncated svd,

    S. L. Shishkin, A. Shalaginov, and S. D. Bopardikar, “Fast approximate truncated svd,”Numerical Linear Algebra with Applications, vol. 26, no. 4, p. e2246, 2019

    2019

  19. [27]

    Random features for large-scale kernel machines,

    A. Rahimi and B. Recht, “Random features for large-scale kernel machines,”Advances in neural information processing systems, vol. 20, 2007

    2007

  20. [28]

    Disentangled condensation for large-scale graphs,

    Z. Xiao, Y . Wang, S. Liu, B. Hu, H. Wang, M. Song, and T. Zheng, “Disentangled condensation for large-scale graphs,” inProceedings of the ACM on Web Conference 2025, 2025, pp. 4494–4506

    2025

  21. [29]

    Herding dynamical weights to learn,

    M. Welling, “Herding dynamical weights to learn,” inProceedings of the 26th annual international conference on machine learning, 2009, pp. 1121–1128

    2009

  22. [30]

    Active learning for convolutional neural networks: A core-set approach,

    O. Sener and S. Savarese, “Active learning for convolutional neural networks: A core-set approach,”arXiv preprint arXiv:1708.00489, 2017

    2017 arXiv

  23. [31]

    Semi-supervised classification with graph convolutional networks,

    T. Kipf, “Semi-supervised classification with graph convolutional networks,”arXiv preprint arXiv:1609.02907, 2016

    2016 arXiv

  24. [32]

    Simplifying graph convolutional networks,

    F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger, “Simplifying graph convolutional networks,” in International conference on machine learning. Pmlr, 2019, pp. 6861–6871

    2019

  25. [33]

    Graph attention networks,

    P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, Y . Bengioet al., “Graph attention networks,”stat, vol. 1050, no. 20, pp. 10–48 550, 2017

    2017

  26. [34]

    Inductive representation learning on large graphs,

    W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,”Advances in neural information processing systems, vol. 30, 2017

    2017

  27. [35]

    Predict then propagate: Graph neural networks meet personalized pagerank,

    J. Gasteiger, A. Bojchevski, and S. G ¨unnemann, “Predict then propagate: Graph neural networks meet personalized pagerank,”arXiv preprint arXiv:1810.05997, 2018

    2018

  28. [36]

    Graph tensor convolutional network,

    L. Wang, Y . Yuan, and X. Luo, “Graph tensor convolutional network,”IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2026

    2026

  29. [37]

    Advanced high-order graph convolutional networks with assorted time-frequency transforms,

    ——, “Advanced high-order graph convolutional networks with assorted time-frequency transforms,”IEEE/CAA Journal of Automatica Sinica, vol. 13, no. 2, pp. 394–408, 2026

    2026

  30. [38]

    A node-collaboration-informed graph convolutional network for highly accurate representation to undirected weighted graph,

    Y . Yuan, Y . Wang, and X. Luo, “A node-collaboration-informed graph convolutional network for highly accurate representation to undirected weighted graph,”IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 6, pp. 11 507–11 519, 2024

    2024

  31. [39]

    Gt-a 2 t: Graph tensor alliance attention network,

    L. Wang, K. Liu, and Y . Yuan, “Gt-a 2 t: Graph tensor alliance attention network,”IEEE/CAA Journal of Automatica Sinica, vol. 12, no. 10, pp. 2165–2167, 2024

    2024

  32. [40]

    A kalman-filter-incorporated latent factor analysis model for temporally dynamic sparse data,

    Y . Yuan, X. Luo, M. Shang, and Z. Wang, “A kalman-filter-incorporated latent factor analysis model for temporally dynamic sparse data,”IEEE Transactions on Cybernetics, vol. 53, no. 9, pp. 5788–5801, 2022

    2022

  33. [41]

    An adaptive divergence-based non-negative latent factor model,

    Y . Yuan, R. Wang, G. Yuan, and L. Xin, “An adaptive divergence-based non-negative latent factor model,”IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 10, pp. 6475–6487, 2023

    2023

  34. [42]

    A multilayered-and-randomized latent factor model for high-dimensional and sparse matrices,

    Y . Yuan, Q. He, X. Luo, and M. Shang, “A multilayered-and-randomized latent factor model for high-dimensional and sparse matrices,”IEEE transactions on big data, vol. 8, no. 3, pp. 784–794, 2020

    2020

  35. [43]

    A generalized and fast-converging non-negative latent factor model for predicting user preferences in recommender systems,

    Y . Yuan, X. Luo, M. Shang, and D. Wu, “A generalized and fast-converging non-negative latent factor model for predicting user preferences in recommender systems,” inProceedings of The Web Conference 2020, 2020, pp. 498–507

    2020

  36. [44]

    Temporal web service qos prediction via kalman filter-incorporated latent factor analysis

    Y . Yuan, M. Shang, and X. Luo, “Temporal web service qos prediction via kalman filter-incorporated latent factor analysis.” inECAI, 2020, pp. 561–568

    2020

  37. [45]

    Non-gradient hash factor learning for high-dimensional and incomplete data representation learning,

    D. Wu, S. Li, Y . He, X. Luo, and X. Gao, “Non-gradient hash factor learning for high-dimensional and incomplete data representation learning,”IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026

    2026

  38. [46]

    Learning accurate representation to nonstandard tensors via a mode-aware tucker network,

    H. Wu, Q. Wang, X. Luo, and Z. Wang, “Learning accurate representation to nonstandard tensors via a mode-aware tucker network,”IEEE Transactions on Knowledge and Data Engineering, 2025

    2025

  39. [47]

    Tensor low-rank orthogonal compression for convolutional neural networks,

    Y . He and X. Luo, “Tensor low-rank orthogonal compression for convolutional neural networks,”IEEE/CAA Journal of Automatica Sinica, vol. 13, no. 1, pp. 227–229, 2026

    2026

  40. [48]

    Discovering spatiotemporal–individual coupled features from nonstandard tensors—a novel dynamic graph mixer approach,

    F. Bi, T. He, Y .-S. Ong, and X. Luo, “Discovering spatiotemporal–individual coupled features from nonstandard tensors—a novel dynamic graph mixer approach,”IEEE Transactions on Neural Networks and Learning Systems, 2025

    2025

  41. [49]

    Scg: A novel spatiotemporal coupling graph convolutional network-incorporated approach for dynamic qos estimation,

    F. Bi and T. He, “Scg: A novel spatiotemporal coupling graph convolutional network-incorporated approach for dynamic qos estimation,” in2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024, pp. 635–640

    2024

  42. [50]

    A convolution bias-incorporated nonnegative latent factorization of tensors model for accurate representation learning to dynamic directed graphs,

    Q. Wang, H. Wu, and X. Luo, “A convolution bias-incorporated nonnegative latent factorization of tensors model for accurate representation learning to dynamic directed graphs,”IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2025

    2025

  43. [51]

    Knowledge-driven multiple instance learning with hierarchical cluster-incorporated aware filtering for larynx pathological grading,

    C. Li, P. Huang, J. Qin, and X. Luo, “Knowledge-driven multiple instance learning with hierarchical cluster-incorporated aware filtering for larynx pathological grading,”IEEE Journal of Biomedical and Health Informatics, 2025

    2025

  44. [52]

    Fmvpci: a multiview fusion neural network for identifying protein complex via fuzzy clustering,

    Y . Yang, L. Hu, G. Li, D. Li, P. Hu, and X. Luo, “Fmvpci: a multiview fusion neural network for identifying protein complex via fuzzy clustering,”IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2025

    2025

  45. [53]

    Graph-based prediction of mirna-drug associations with multisource information and metapath enhancement matrices,

    M.-Y . Wu, P.-W. Hu, Z.-H. You, J. Zhang, L. Hu, and X. Luo, “Graph-based prediction of mirna-drug associations with multisource information and metapath enhancement matrices,”IEEE Journal of Biomedical and Health Informatics, 2025

    2025

  46. [54]

    Fuzzy mixture-of-experts aggregation for organoid identification with multiscale state space features,

    X. Deng, P. Hu, T. Herget, F. Tan, X. Zhu, J. Zhang, Y .-a. Huang, L. Hu, Z. You, and X. Luo, “Fuzzy mixture-of-experts aggregation for organoid identification with multiscale state space features,”IEEE Transactions on Fuzzy Systems, vol. 34, no. 1, pp. 324–335, 2025

    2025

  47. [55]

    Ncsac: Effective neural community search via attribute-augmented conductance,

    L. Lin, Q. Li, M. Qiao, Z. Wang, J. Zhao, R.-H. Li, X. Luo, and T. Jia, “Ncsac: Effective neural community search via attribute-augmented conductance,”IEEE Transactions on Knowledge and Data Engineering, vol. 38, no. 2, pp. 1221–1235, 2025

    2025

  48. [56]

    A proximal-admm-incorporated nonnegative latent-factorization-of-tensors model for representing dynamic cryptocurrency transaction network,

    X. Liao, H. Wu, T. He, and X. Luo, “A proximal-admm-incorporated nonnegative latent-factorization-of-tensors model for representing dynamic cryptocurrency transaction network,”IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2025

    2025

  49. [57]

    Multi-scale collaborative distillation graph neural networks for session-based recommendation,

    J. Gou, Y . Cheng, B. Ma, L. Du, X. Luo, and Z. Yi, “Multi-scale collaborative distillation graph neural networks for session-based recommendation,”IEEE Transactions on Services Computing, 2025

    2025

  50. [58]

    A generalized nesterov’s accelerated gradient-incorporated non-negative latent- factorization-of-tensors model for efficient representation to dynamic qos data,

    M. Chen, R. Wang, Y . Qiao, and X. Luo, “A generalized nesterov’s accelerated gradient-incorporated non-negative latent- factorization-of-tensors model for efficient representation to dynamic qos data,”IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8,...

    2024

  51. [59]

    Symmetry and graph bi-regularized non-negative matrix factorization for precise community detection,

    Z. Liu, X. Luo, and M. Zhou, “Symmetry and graph bi-regularized non-negative matrix factorization for precise community detection,”IEEE Transactions on Automation Science and Engineering, vol. 21, no. 2, pp. 1406–1420, 2023

    2023

  52. [60]

    Mmlf: Multi-metric latent feature analysis for high-dimensional and incomplete data,

    D. Wu, P. Zhang, Y . He, and X. Luo, “Mmlf: Multi-metric latent feature analysis for high-dimensional and incomplete data,”IEEE transactions on services computing, vol. 17, no. 2, pp. 575–588, 2023

    2023

  53. [61]

    Learning error refinement in stochastic gradient descent-based latent factor analysis via diversified pid controllers,

    J. Li, Y . Yuan, and X. Luo, “Learning error refinement in stochastic gradient descent-based latent factor analysis via diversified pid controllers,”IEEE Transactions on Emerging Topics in Computational Intelligence, 2025

    2025

  54. [62]

    A fuzzy pid-incorporated stochastic gradient descent algorithm for fast and accurate latent factor analysis,

    Y . Yuan, J. Li, and X. Luo, “A fuzzy pid-incorporated stochastic gradient descent algorithm for fast and accurate latent factor analysis,”IEEE Transactions on Fuzzy Systems, vol. 32, no. 7, pp. 4049–4061, 2024

    2024

  55. [63]

    Adaptively-accelerated parallel stochastic gradient descent for high-dimensional and incomplete data representation learning,

    W. Qin, X. Luo, and M. Zhou, “Adaptively-accelerated parallel stochastic gradient descent for high-dimensional and incomplete data representation learning,”IEEE Transactions on Big Data, vol. 10, no. 1, pp. 92–107, 2023

    2023

  56. [64]

    Sgd-dyg: Self-reliant global dependency apprehending on dynamic graphs,

    M. Han, L. Wang, Y . Yuan, and X. Luo, “Sgd-dyg: Self-reliant global dependency apprehending on dynamic graphs,” in Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V . 2, 2025, pp. 802–813

    2025

  57. [65]

    Auto-encoding neural tucker factorization,

    P. Tang, X. Luo, and J. Woodcock, “Auto-encoding neural tucker factorization,”IEEE Transactions on Knowledge and Data Engineering, 2025

    2025

  58. [66]

    Multimetric autoencoder for representing high-dimensional and incomplete data,

    D. Wu, C. Liang, Y . He, Y . Qiao, and X. Luo, “Multimetric autoencoder for representing high-dimensional and incomplete data,”IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 56, no. 3, pp. 1533–1546, 2026

    2026

  59. [67]

    A novel tensor causal convolution network model for highly-accurate representation to spatio-temporal data,

    X. Liao, H. Wu, and X. Luo, “A novel tensor causal convolution network model for highly-accurate representation to spatio-temporal data,”IEEE Transactions on Automation Science and Engineering, 2025

    2025

  60. [68]

    Adaptive divergence-based non-negative latent factor analysis of high-dimensional and incomplete matrices from industrial applications,

    Y . Yuan, X. Luo, and M. Zhou, “Adaptive divergence-based non-negative latent factor analysis of high-dimensional and incomplete matrices from industrial applications,”IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 2, pp. 1209–1222, 2024

    2024

  61. [69]

    An adaptively bias-extended non-negative latent factorization of tensors model for accurately representing the dynamic qos data,

    X. Xu, M. Lin, X. Luo, and Z. Xu, “An adaptively bias-extended non-negative latent factorization of tensors model for accurately representing the dynamic qos data,”IEEE Transactions on Services Computing, 2025

    2025

  62. [70]

    A robust coevolutionary neural-based optimization algorithm for constrained nonconvex optimization,

    L. Wei, L. Jin, and X. Luo, “A robust coevolutionary neural-based optimization algorithm for constrained nonconvex optimization,”IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 6, pp. 7778–7791, 2022

    2022

  63. [71]

    A scalable multichannel sentiment analysis model with enhanced semantic understanding and redundancy reduction,

    J. Liu, X. Li, M. Lin, and X. Luo, “A scalable multichannel sentiment analysis model with enhanced semantic understanding and redundancy reduction,”IEEE Transactions on Computational Social Systems, 2025

    2025

  64. [72]

    A novel tensor decomposition-based efficient detector for low-altitude aerial objects with knowledge distillation scheme,

    N. Zeng, X. Li, P. Wu, H. Li, and X. Luo, “A novel tensor decomposition-based efficient detector for low-altitude aerial objects with knowledge distillation scheme,”IEEE/CAA Journal of Automatica Sinica, vol. 11, no. 2, pp. 487–501, 2024

    2024

  65. [73]

    A fast nonnegative autoencoder-based approach to latent feature analysis on high-dimensional and incomplete data,

    F. Bi, T. He, and X. Luo, “A fast nonnegative autoencoder-based approach to latent feature analysis on high-dimensional and incomplete data,”IEEE Transactions on Services Computing, vol. 17, no. 3, pp. 733–746, 2023

    2023

  66. [74]

    A sampling-neighborhood-regularized latent factorization of tensor for dynamic qos estimation,

    X. Xu, M. Lin, Z. Xu, and X. Luo, “A sampling-neighborhood-regularized latent factorization of tensor for dynamic qos estimation,”IEEE Transactions on Network and Service Management, vol. 23, pp. 1707–1722, 2025

    2025

  67. [75]

    Attention-mechanism-based neural latent-factorization-of-tensors model,

    ——, “Attention-mechanism-based neural latent-factorization-of-tensors model,”ACM Transactions on Knowledge Dis- covery from Data, vol. 19, no. 4, pp. 1–27, 2025

    2025

  68. [76]

    Graph linear convolution pooling for learning in incomplete high-dimensional data,

    F. Bi, T. He, Y .-S. Ong, and X. Luo, “Graph linear convolution pooling for learning in incomplete high-dimensional data,” IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 4, pp. 1838–1852, 2024

    2024

  69. [77]

    An outlier-resilient autoencoder for representing high-dimensional and incomplete data,

    D. Wu, Y . Hu, K. Liu, J. Li, X. Wang, S. Deng, N. Zheng, and X. Luo, “An outlier-resilient autoencoder for representing high-dimensional and incomplete data,”IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 2, pp. 1379–1391, 2024

    2024

  70. [78]

    Robust low-rank latent feature analysis for spatiotemporal signal recovery,

    D. Wu, Z. Li, Z. Yu, Y . He, and X. Luo, “Robust low-rank latent feature analysis for spatiotemporal signal recovery,” IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 2, pp. 2829–2842, 2023

    2023

  71. [79]

    Neural tucker factorization,

    P. Tang and X. Luo, “Neural tucker factorization,”IEEE/CAA Journal of Automatica Sinica, vol. 12, no. 2, pp. 475–477, 2025

    2025

  72. [80]

    Link-based attributed graph clustering via approximate generative bayesian learning,

    Y . Yang, L. Hu, G. Li, D. Li, P. Hu, and X. Luo, “Link-based attributed graph clustering via approximate generative bayesian learning,”IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2025

    2025

  73. [81]

    Neural nonnegative latent factorization of tensors model with acceleration and unconstraint,

    W. Li, M. Lin, X. Xu, L. Lin, Z. Xu, and X. Luo, “Neural nonnegative latent factorization of tensors model with acceleration and unconstraint,”IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2025

    2025

  74. [82]

    Latent factor analysis model with temporal regularized constraint for road traffic data imputation,

    H. Yang, M. Lin, H. Chen, X. Luo, and Z. Xu, “Latent factor analysis model with temporal regularized constraint for road traffic data imputation,”IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 1, pp. 724–741, 2024

    2024

  75. [83]

    Local search-based anytime algorithms for continuous distributed constraint optimization problems,

    X. Liao, K. Hoang, and X. Luo, “Local search-based anytime algorithms for continuous distributed constraint optimization problems,”IEEE/CAA Journal of Automatica Sinica, vol. 12, no. 1, pp. 288–290, 2025

    2025

  76. [84]

    Local search-based anytime algorithms for continuous distributed constraint optimization problems,

    ——, “Local search-based anytime algorithms for continuous distributed constraint optimization problems,”IEEE/CAA Journal of Automatica Sinica, vol. 12, no. 1, pp. 288–290, 2025

    2025

  77. [85]

    Iterative role negotiation via the bilevel gra++ with decision tolerance,

    Q. Jiang, D. Liu, H. Zhu, S. Wu, N. Wu, X. Luo, and Y . Qiao, “Iterative role negotiation via the bilevel gra++ with decision tolerance,”IEEE Transactions on Computational Social Systems, vol. 11, no. 6, pp. 7484–7499, 2024

    2024

Pith tools

Reviewed June 29, 2026 · model on record in the stance chip above.