Pith. sign in

REVIEW 4 major objections 3 minor 49 references

MPOCryptoML: Multi-Pattern based Off-Chain Crypto Money Laundering Detection

T0 review · 4 major / 3 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read MPOCryptoML detects five laundering patterns in cryptocurrency transactions and reports consistent gains over GNN-based detectors on three public datasets.

desk verdict Only the abstract is readable, so the reported gains are unverifiable; the pattern-aware design is plausible but needs a clean resubmission to be fairly judged. read the letter →

arxiv 2508.12641 v1 pith:GNKHWFBU submitted 2025-08-18 cs.CR

Yasaman Samadi , Hai Dong , Xiaoyu Xia This is my paper · ORCID
classification cs.CR
keywords cryptocurrencymoneylaunderingtransactiongraphanalysisPersonalizedPageRankpatterndetectionanomalyscoreneuralnetworksElliptic++Ethereumfraud
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

This paper argues that existing graph-based laundering detectors miss important money laundering patterns, and that explicitly targeting a set of patterns closes those gaps. It proposes MPOCryptoML, which combines multi-source Personalized PageRank with timestamp- and weight-based heuristics for five structures—fan-in, fan-out, bipartite, gather-scatter, and stack—and fuses the signals into an anomaly score for ranking accounts. On three public transaction datasets, it reports consistent gains over GNN baselines, with improvements up to about 10 percent in accuracy, recall, and related metrics. If true, the result suggests that pattern coverage itself, not just model capacity, drives detection performance.

What carries the argument

The load-bearing object is the multi-source Personalized PageRank score combined with two hand-coded structural heuristics. Personalized PageRank ranks nodes by the stationary probability of random walks that restart from seed accounts, and the multi-source version takes seeds from multiple suspected sources so diffuse, random-looking laundering structures can be caught. The two novel algorithms exploit the observation that laundering structures leave timestamp and transaction-weight signatures inside high-volume financial subnetworks, letting the model flag fan-in, fan-out, bipartite, gather-scatter, and stack shapes without learning them from labelled examples. A logistic regression over the pattern scores models how patterns correlate, and the final anomaly score function sums these signals to produce an account ranking.

What would settle it

Run MPOCryptoML on a publicly labeled transaction dataset that contains a laundering structure outside the five patterns—for example, a long sequential layering chain with normal timestamps and weights and no fan-in, fan-out, bipartite, gather-scatter, or stack signature. If such accounts receive anomaly scores no higher than random benign accounts while a GNN baseline ranks them correctly, the claim that pattern coverage drives the gains would be undermined.

Watch

Extended reading notes

Core claim

The paper's central claim is that a detector which explicitly models multiple laundering patterns can outperform generic graph neural network detectors on cryptocurrency transaction data. The authors assert that existing models leave specific transactional structures unmodelled, and that each omitted pattern creates a detectable gap. MPOCryptoML operationalizes this by scoring each account with three complementary modules—multi-source Personalized PageRank for diffuse structures, two algorithms that read timestamps and weights in high-volume financial subnetworks to identify fan-in, fan-out, bipartite, gather-scatter, and stack patterns, and a logistic regression that captures correlations among patterns—then combining them into an anomaly score. The reported experiments on Elliptic++, Ethereum fraud, and Wormhole datasets show improvements over prior GNN-based approaches, up to 9.13% in precision, 10.16% in recall, 7.63% in F1-score, and 10.19% in accuracy.

Load-bearing premise

The load-bearing assumption is that the hand-coded timestamp and weight heuristics, together with multi-source Personalized PageRank, separate the five laundering patterns from benign high-volume financial behavior in the three datasets, and that these five patterns cover the laundering structures actually present.

Editorial extensions

If this is right

  • If MPOCryptoML's reported gains hold, adding explicit pattern detectors to transaction-graph models should improve precision, recall, and F1-score over purely learned graph representations.
  • The anomaly-score ranking means the method can be used as a screening layer, letting analysts focus on the highest-ranked accounts rather than classifying every node.
  • The logistic-regression pattern-correlation module implies pattern co-occurrence is predictable, so evidence of one pattern can strengthen suspicion of another.
  • The results across Elliptic++, Ethereum fraud, and Wormhole datasets indicate the approach can transfer across different public transaction graphs rather than fitting a single dataset.

Reading between the lines

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

  • A test on a dataset labeled with a laundering structure outside the five—such as a long sequential layering chain with normal timestamps and weights—would reveal whether the advantage comes from pattern coverage or from the extra features and scores the model adds.
  • Because the timestamp and weight heuristics are hand-coded, their portability across blockchains with different fee markets, block times, and confirmation lags is an open question; a cross-chain transfer test would show how much re-tuning the pattern signatures need.
  • The logistic-regression correlation module could also be read as a descriptive tool: on labeled data it can map which laundering patterns co-occur, which may help build or refine laundering typologies.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 3 minor

Summary. The paper proposes MPOCryptoML, an off-chain cryptocurrency money-laundering detector built from a multi-source Personalized PageRank module, timestamp/weight heuristics for five transaction patterns (fan-in, fan-out, bipartite, gather-scatter, stack), a logistic-regression pattern-correlation module, and an anomaly-score ranking. On Elliptic++, Ethereum fraud, and Wormhole transaction data, the abstract reports improvements over GNN-based detectors of up to 9.13% in precision, 10.16% in recall, 7.63% in F1, and 10.19% in accuracy. As supplied, however, the full text is unreadable encoding garbage, so the algorithms, equations, threshold choices, experimental setup, baselines, tables, and detailed results cannot be checked.

Significance. If the empirical claims were supported, the paper would offer a useful interpretable alternative to GNN-based laundering detection, with an explicit pattern taxonomy and an anomaly-ranking output. The choice of three public datasets is appropriate, and the pattern-explicit framing is a genuine contribution to the presentation of the problem. However, the accessible evidence is limited to the abstract: no error bars, dataset sizes, ablations, statistical tests, code, or hyperparameter analysis are visible. The central superiority claim is therefore not yet established; the paper currently reads as a proposal rather than a verified method.

major comments (4)
  1. [Full text (all pages)] The full text supplied for review is an unreadable encoding artifact: sentences, equations, and tables are replaced by sequences of replacement characters. Because the methods, proofs, dataset splits, baselines, and result tables are not accessible, none of the central claims can be verified. This is the single most important defect; please resubmit a clean, machine-readable manuscript.
  2. [Abstract (results)] The abstract's headline numbers (up to 9.13% precision, 10.16% recall, 7.63% F1, and 10.19% accuracy) are reported without sample size, number of independent runs, variance, or significance tests. The phrase 'up to' suggests best-case selection; without per-dataset and per-metric tables with means, standard deviations, and statistical comparisons, the claim of consistent performance gains is not established.
  3. [Abstract (logistic regression and anomaly score)] The pipeline uses detected patterns as inputs to a logistic-regression correlation model and an anomaly-score ranking, while the pattern-detection thresholds and Personalized PageRank source weights are free parameters. If any of these choices are made using test labels, the comparison against GNN baselines could reflect in-sample fitting rather than pattern-detection skill. Please report how thresholds and weights are selected, use nested or outer cross-validation, and include ablations (single-pattern modules, no logistic regression, no anomaly integration) with identical splits and equivalent tuning budgets for baselines.
  4. [Abstract (pattern taxonomy)] The abstract asserts that neglecting any of the five patterns creates detection gaps, but it does not define the patterns or justify their completeness. In particular, the reader cannot see how the Elliptic++, Ethereum fraud, and Wormhole ground-truth labels map to fan-in, fan-out, bipartite, gather-scatter, and stack structures, or whether laundering structures outside the taxonomy are captured by the multi-source Personalized PageRank module. Please provide formal pattern definitions, label-alignment analysis, and coverage statistics for each dataset.
minor comments (3)
  1. [Document header] The document text contains the line 'arXiv:2508.12642v1 [cond-mat.mtrl-sci] 18 Aug 2025', which is unrelated to the cs.CR submission arXiv:2508.12641; this indicates a file conversion or upload error that must be corrected.
  2. [Abstract (efficiency claim)] The abstract states that the results 'validate the efficacy and efficiency' of MPOCryptoML, but no runtime, complexity, or scalability data are visible in the readable portion of the submission; please add complexity analysis or runtime tables.
  3. [Reproducibility] No code repository, fixed data splits, or seed information is identifiable; for reproducibility on three public datasets, the authors should provide exact split definitions and, ideally, an implementation.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity can be substantiated from the readable portion of the manuscript; the full text is unreadable and no specific equation or fitted-parameter reduction is available to exhibit.

full rationale

The only readable material is the abstract. The abstract claims a multi-source Personalized PageRank step, timestamp and weight heuristics for fan-in, fan-out, bipartite, gather-scatter, and stack patterns, a logistic regression examining correlations, and an anomaly-score ranking. To establish circularity under the stated rules I would need to quote a specific reduction, for example a threshold fitted on labels later used as the predicted score, or a pattern defined in terms of the target label, or a self-citation invoked as the only support for a forced choice. None of those reductions is visible in the supplied text; the body of the paper is corrupted beyond recovery. The possibility that the logistic regression is trained on labels used to select pattern thresholds is noted, but the hard rule forbids speculation about author intent and requires exhibiting the reduction. Since no equation, algorithm pseudocode, or evaluation-split description is readable, no circular step can be quoted. The correct outcome is therefore an honest non-finding: the submission is not shown to be circular on the evidence available. This should not be read as endorsing the empirical claims; it only means that circularity is not established.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

Because only the abstract was readable, this ledger lists the parameters and domain assumptions implied by the abstract; exact values and definitions are not provided.

free parameters (4)
  • Personalized PageRank restart probability and source weighting
    The multi-source Personalized PageRank algorithm requires a restart parameter and source selection; values are not given in the abstract and would typically be tuned.
  • Timestamp and weight pattern detection thresholds
    The two timestamp/weight algorithms must use thresholds or window sizes to declare fan-in, fan-out, bipartite, gather-scatter, and stack patterns; not stated.
  • Logistic regression coefficients
    The correlation module fits coefficients to labeled data to measure pattern correlations.
  • Anomaly score integration weights
    The final anomaly score combines module outputs, and the weights are not stated in the abstract.
assumptions (3)
  • domain assumption The five enumerated patterns (fan-in, fan-out, bipartite, gather-scatter, stack) cover the meaningful off-chain money laundering structures.
    The abstract claims that neglecting any laundering pattern introduces detection gaps, which presumes this pattern taxonomy is complete and relevant to off-chain crypto.
  • domain assumption Timestamp and transaction weight are sufficient features to distinguish laundering structures in high-volume financial networks.
    The two novel algorithms are built entirely on timestamp and weight analysis, so this feature sufficiency is load-bearing.
  • domain assumption The public datasets' labels are valid ground truth for off-chain money laundering.
    The experiments use Elliptic++, Ethereum fraud, and Wormhole datasets; the labels must correspond to real laundering activity for the reported gains to be meaningful.

how reviews work

0 comments
Cite this review

Pith. "Pith review of MPOCryptoML: Multi-Pattern based Off-Chain Crypto Money Laundering Detection." pith.science (2026). https://pith.science/paper/GNKHWFBU

@misc{pith2026250812641,
  author       = {Pith},
  title        = {Pith review of: MPOCryptoML: Multi-Pattern based Off-Chain Crypto Money Laundering Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GNKHWFBU}},
  note         = {Machine review of arXiv:2508.12641}
}
read the original abstract

Recent advancements in money laundering detection have demonstrated the potential of using graph neural networks to capture laundering patterns accurately. However, existing models are not explicitly designed to detect the diverse patterns of off-chain cryptocurrency money laundering. Neglecting any laundering pattern introduces critical detection gaps, as each pattern reflects unique transactional structures that facilitate the obfuscation of illicit fund origins and movements. Failure to account for these patterns may result in under-detection or omission of specific laundering activities, diminishing model accuracy and allowing schemes to bypass detection. To address this gap, we propose the MPOCryptoML model to effectively detect multiple laundering patterns in cryptocurrency transactions. MPOCryptoML includes the development of a multi-source Personalized PageRank algorithm to identify random laundering patterns. Additionally, we introduce two novel algorithms by analyzing the timestamp and weight of transactions in high-volume financial networks to detect various money laundering structures, including fan-in, fan-out, bipartite, gather-scatter, and stack patterns. We further examine correlations between these patterns using a logistic regression model. An anomaly score function integrates results from each module to rank accounts by anomaly score, systematically identifying high-risk accounts. Extensive experiments on public datasets including Elliptic++, Ethereum fraud detection, and Wormhole transaction datasets validate the efficacy and efficiency of MPOCryptoML. Results show consistent performance gains, with improvements up to 9.13% in precision, up to 10.16% in recall, up to 7.63% in F1-score, and up to 10.19% in accuracy.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

49 extracted references · 45 canonical work pages

  1. [1]

    G. Yang, X. Liu, and B. Li, ``Anti-money laundering supervision by intelligent algorithm,'' Computers and Security, p. 103344, 2023

    work page 2023

  2. [2]

    U. Nations. (2023) Money Laundering Through Cryptocurrencies . [Online]. Available: https://syntheticdrugs.unodc.org/syntheticdrugs/en/cybercrime/launderingproceeds/moneylaundering.html

    work page 2023

  3. [3]

    X. Sun, W. Feng, S. Liu, Y. Xie, S. Bhatia, B. Hooi, W. Wang, and X. Cheng, ``Monlad: Money laundering agents detection in transaction streams,'' in Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (WSDM), 2022, pp. 976--986

    work page 2022

  4. [4]

    Alliance

    F. Alliance. (2022) Criminal Abuse of Digital Currencies . [Online]. Available: https://www.austrac.gov.au/sites/default/files/2022-04/AUSTRAC_FCG_PreventingCriminalAbuseOfDigitalCurrencies_FINAL.pdf

    work page 2022

  5. [5]

    C. Team. (2024) Case Study: How We Track Crypto Money Laundering for Off-Chain Crime . [Online]. Available: https://www.chainalysis.com/blog/2024-crypto-money-laundering/#: :text=The

    work page 2024

  6. [6]

    Kolachala, E

    K. Kolachala, E. Simsek, M. Ababneh, and R. Vishwanathan, ``Sok: Money laundering in cryptocurrencies,'' in Proceedings of the 16th International Conference on Availability, Reliability and Security, 2021, pp. 1--10

    work page 2021

  7. [7]

    F. T. Liu, K. M. Ting, and Z.-H. Zhou, ``Isolation-based anomaly detection,'' ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 6, no. 1, pp. 1--39, 2012

    work page 2012

  8. [8]

    M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, ``Lof: Identifying density-based local outliers,'' in Proceedings of the 2000 ACM SIGMOD international conference on Management of data, 2000, pp. 93--104

    work page 2000

Show all 49 references
  1. [9]

    L. Ruff, R. Vandermeulen, N. Goernitz, L. Deecke, S. A. Siddiqui, A. Binder, E. M \"u ller, and M. Kloft, ``Deep one-class classification,'' in International conference on machine learning. 1em plus 0.5em minus 0.4em PMLR, 2018, pp. 4393--4402

    2018

  2. [10]

    Z. Li, Y. Zhao, X. Hu, N. Botta, C. Ionescu, and G. H. Chen, ``Ecod: Unsupervised outlier detection using empirical cumulative distribution functions,'' IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12\,181--12\,193, 2022

    2022

  3. [11]

    B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen, ``Deep autoencoding gaussian mixture model for unsupervised anomaly detection,'' in International conference on learning representations, 2018

    2018

  4. [12]

    H. Ju, D. Lee, J. Hwang, J. Namkung, and H. Yu, ``Pumad: Pu metric learning for anomaly detection,'' Information Sciences, vol. 523, pp. 167--183, 2020

    2020

  5. [13]

    H. Mu, R. Sun, G. Yuan, and G. Shi, ``Positive unlabeled learning-based anomaly detection in videos,'' International Journal of Intelligent Systems, vol. 36, no. 8, pp. 3767--3788, 2021

    2021

  6. [14]

    B. Tian, Q. Su, and J. Yin, ``Anomaly detection by leveraging incomplete anomalous knowledge with anomaly-aware bidirectional gans,'' arXiv preprint arXiv:2204.13335, 2022

    2022 arXiv

  7. [15]

    W. Zong, F. Zhou, M. Pavlovski, and W. Qian, ``Peripheral instance augmentation for end-to-end anomaly detection using weighted adversarial learning,'' in International Conference on Database Systems for Advanced Applications. 1em plus 0.5em minus 0.4em Springer, 2022, pp. 506--522

    2022

  8. [16]

    F. Chen, Y. Zhang, Z. Qin, L. Fan, R. Jiang, Y. Liang, Q. Wen, and S. Deng, ``Learning multi-pattern normalities in the frequency domain for efficient time series anomaly detection,'' in 2024 IEEE 40th International Conference on Data Engineering (ICDE). 1em plus 0.5em minus 0...

    2024

  9. [17]

    Tang and J

    J. Tang and J. Yin, ``Developing an intelligent data discriminating system of anti-money laundering based on svm,'' in 2005 International conference on machine learning and cybernetics(ACM), vol. 6. 1em plus 0.5em minus 0.4em IEEE, 2005, pp. 3453--3457

    2005

  10. [18]

    Michalak and J

    K. Michalak and J. Korczak, ``Graph mining approach to suspicious transaction detection,'' in 2011 Federated conference on computer science and information systems (FedCSIS). 1em plus 0.5em minus 0.4em IEEE, 2011, pp. 69--75

    2011

  11. [19]

    L.-T. Lv, N. Ji, and J.-L. Zhang, ``A rbf neural network model for anti-money laundering,'' in 2008 International conference on wavelet analysis and pattern recognition, vol. 1. 1em plus 0.5em minus 0.4em IEEE, 2008, pp. 209--215

    2008

  12. [20]

    E. L. Paula, M. Ladeira, R. N. Carvalho, and T. Marzag \ a o, ``Deep learning anomaly detection as support fraud investigation in brazilian exports and anti-money laundering,'' in 2016 15th ieee international conference on machine learning and applications (icmla). 1em plus 0....

    2016

  13. [21]

    X. Li, S. Liu, Z. Li, X. Han, C. Shi, B. Hooi, H. Huang, and X. Cheng, ``Flowscope: Spotting money laundering based on graphs,'' in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, 2020, pp. 4731--4738

    2020

  14. [22]

    X. Li, Y. Li, X. Mo, H. Xiao, Y. Shen, and L. Chen, ``Diga: Guided diffusion model for graph recovery in anti-money laundering,'' in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023, pp. 4404--4413

    2023

  15. [23]

    Z. Chai, Y. Yang, J. Dan, S. Tian, C. Meng, W. Wang, and Y. Sun, ``Towards learning to discover money laundering sub-network in massive transaction network,'' 2023

    2023

  16. [24]

    X. Luo, J. Wu, A. Beheshti, J. Yang, X. Zhang, Y. Wang, and S. Xue, ``Comga: Community-aware attributed graph anomaly detection,'' in Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (WSDM), 2022, pp. 657--665

    2022

  17. [25]

    Wyatt, A

    J. Wyatt, A. Leach, S. M. Schmon, and C. G. Willcocks, ``Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise,'' in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 650--656

    2022

  18. [26]

    H. Wang, C. Zhou, J. Wu, W. Dang, X. Zhu, and J. Wang, ``Deep structure learning for fraud detection,'' in 2018 IEEE international conference on data mining (ICDM). 1em plus 0.5em minus 0.4em IEEE, 2018, pp. 567--576

    2018

  19. [27]

    Kumagai, T

    A. Kumagai, T. Iwata, and Y. Fujiwara, ``Semi-supervised anomaly detection on attributed graphs,'' in 2021 International Joint Conference on Neural Networks (IJCNN). 1em plus 0.5em minus 0.4em IEEE, 2021, pp. 1--8

    2021

  20. [28]

    C. Team. (2023) Case Study: How We Track Crypto Money Laundering for Off-Chain Crime . [Online]. Available: https://www.chainalysis.com/blog/japan-cryptocurrency-money-laundering-case-study/

    2023

  21. [29]

    E. S. Clare Sullivan. (2023) Trade-based Money Laundering: Risks and Regulatory Responses . [Online]. Available: https://www.aic.gov.au/sites/default/files/2020-05/rpp115.pdf

    2023

  22. [30]

    Altman, B

    E. Altman, B. Egressy, J. Blanu s a, and K. Atasu, ``Realistic synthetic financial transactions for anti-money laundering models,'' arXiv preprint arXiv:2306.16424, 2023

    2023 arXiv

  23. [31]

    Dupuis and K

    D. Dupuis and K. Gleason, ``Money laundering with cryptocurrency: Open doors and the regulatory dialectic,'' Journal of Financial Crime, vol. 28, no. 1, pp. 60--74, 2020

    2020

  24. [32]

    S. Jones. (2023) CRegulation of Digital and Crypto Assets . [Online]. Available: https://ministers.treasury.gov.au/ministers/stephen-jones-2022/media-releases/regulation-digital-and-crypto-assets

    2023

  25. [33]

    S. Wang, R. Yang, X. Xiao, Z. Wei, and Y. Yang, ``Fora: Simple and effective approximate single-source personalized pagerank,'' in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, pp. 505--514

    2017

  26. [34]

    S. Luo, X. Xiao, W. Lin, and B. Kao, ``Baton: Batch one-hop personalized pageranks with efficiency and accuracy,'' IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 10, pp. 1897--1908, 2019

    1908

  27. [35]

    Weber, G

    M. Weber, G. Domeniconi, J. Chen, D. K. I. Weidele, C. Bellei, T. Robinson, and C. E. Leiserson, ``Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics,'' arXiv preprint arXiv:1908.02591, 2019

    1908 arXiv

  28. [36]

    H. Kim, B. S. Lee, W.-Y. Shin, and S. Lim, ``Graph anomaly detection with graph neural networks: Current status and challenges,'' IEEE Access, vol. 10, pp. 111\,820--111\,829, 2022

    2022

  29. [37]

    X. Ma, J. Wu, S. Xue, J. Yang, C. Zhou, Q. Z. Sheng, H. Xiong, and L. Akoglu, ``A comprehensive survey on graph anomaly detection with deep learning,'' IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12\,012--12\,038, 2021

    2021

  30. [38]

    Ma and M

    W. Ma and M. A. Lejeune, ``A distributionally robust area under curve maximization model,'' Operations Research Letters, vol. 48, no. 4, pp. 460--466, 2020

    2020

  31. [39]

    K. Liu, Y. Dou, X. Ding, X. Hu, R. Zhang, H. Peng, L. Sun, and S. Y. Philip, ``Pygod: A python library for graph outlier detection,'' Journal of Machine Learning Research, vol. 25, no. 141, pp. 1--9, 2024

    2024

  32. [40]

    K. Ding, J. Li, N. Agarwal, and H. Liu, ``Inductive anomaly detection on attributed networks,'' in Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence (IJCAI), 2021, pp. 1288--1294

    2021

  33. [41]

    Zheng, M

    Y. Zheng, M. Jin, Y. Liu, L. Chi, K. T. Phan, and Y.-P. P. Chen, ``Generative and contrastive self-supervised learning for graph anomaly detection,'' IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12\,220--12\,233, 2021

    2021

  34. [42]

    K. Liu, Y. Dou, Y. Zhao, X. Ding, X. Hu, R. Zhang, K. Ding, C. Chen, H. Peng, K. Shu et al., ``Bond: Benchmarking unsupervised outlier node detection on static attributed graphs,'' Advances in Neural Information Processing Systems (NeurIPS), vol. 35, pp. 27\,021--27\,035, 2022

    2022

  35. [43]

    Wolleb, F

    J. Wolleb, F. Bieder, R. Sandk \"u hler, and P. C. Cattin, ``Diffusion models for medical anomaly detection,'' in International Conference on Medical image computing and computer-assisted intervention. 1em plus 0.5em minus 0.4em Springer, 2022, pp. 35--45

    2022

  36. [44]

    H. Xu, W. Chen, N. Zhao, Z. Li, J. Bu, Z. Li, Y. Liu, Y. Zhao, D. Pei, Y. Feng et al., ``Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications,'' in Proceedings of the 2018 world wide web conference (WWW), 2018, pp. 187--196

    2018

  37. [45]

    Y. Su, Y. Zhao, C. Niu, R. Liu, W. Sun, and D. Pei, ``Robust anomaly detection for multivariate time series through stochastic recurrent neural network,'' in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (KDD), 2019, pp. 2828--2837

    2019

  38. [46]

    S. Tuli, G. Casale, and N. R. Jennings, ``Tranad: Deep transformer networks for anomaly detection in multivariate time series data,'' arXiv preprint arXiv:2201.07284, 2022

    2022 arXiv

  39. [47]

    Jullum, A

    M. Jullum, A. L land, R. B. Huseby, G. nonsen, and J. Lorentzen, ``Detecting money laundering transactions with machine learning,'' Journal of Money Laundering Control, vol. 23, no. 1, pp. 173--186, 2020

    2020

  40. [48]

    C. Qiu, M. Kloft, S. Mandt, and M. Rudolph, ``Raising the bar in graph-level anomaly detection,'' arXiv preprint arXiv:2205.13845, 2022

    2022 arXiv

  41. [49]

    Li and L

    X. Li and L. Chen, ``Graph anomaly detection with domain-agnostic pre-training and few-shot adaptation,'' in 2024 IEEE 40th International Conference on Data Engineering (ICDE). 1em plus 0.5em minus 0.4em IEEE, 2024, pp. 2667--2680

    2024

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.