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Towards Adversarially Robust Multigraph Neural Networks for Financial Crime Analysis

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This paper introduces the first scalable framework for structural attacks and adversarial training of multigraph neural networks using node-edge incidence matrices, with a cycle-based regularizer for attack plausibility. Experiments show these models are vulnerable to learned structural attacks and that adversarial training consistently improves robustness.

SourcearXiv Machine LearningAuthor: Dennis Heijmans, H. \c{C}a\u{g}r{\i} Bilgi, Rui Wang, Kubilay Atasu
Towards Adversarially Robust Multigraph Neural Networks for Financial Crime Analysis
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[Submitted on 22 Oct 2026]

Title:Towards Adversarially Robust Multigraph Neural Networks for Financial Crime Analysis

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Abstract:Financial transaction networks are naturally represented as multigraphs, where repeated transactions between the same pair of accounts are modeled as distinct edges. Recently proposed multigraph neural network architectures leverage this structure explicitly to achieve a strong performance on anti-money laundering (AML) tasks. However, their robustness to adversaries that manipulate transaction structures to evade detection remains largely unexplored. We present the first scalable framework for structural attacks and adversarial training of multigraph neural networks using node-edge incidence matrices, adapting projected gradient descent and projected randomized block coordinate descent methods to optimize structural perturbations over such matrices. We further introduce a cycle-based regularizer that penalizes changes in the global counts of directed 2- and 3-cycles, providing a structural measure of attack plausibility. Experiments on synthetic pattern-detection and realistic AML datasets show that our learned attacks substantially outperform non-learnable perturbation baselines in reducing detection performance (PR-AUC), while adversarial training consistently improves robustness against these attacks. These results demonstrate that multigraph neural networks are vulnerable to learned structural attacks and that adversarial training provides an effective defense. Our code is available at this https URL.

Comments: 20 pages, 10 figures, 3 tables. Accepted at the Fifth Learning on Graphs Conference (LoG 2026). Code: this https URL

Subjects:

Machine Learning (cs.LG)

Cite as: arXiv:2610.31718 [cs.LG]

(or arXiv:2610.31718v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.31718

arXiv-issued DOI via DataCite

Submission history

From: Dennis Heijmans [view email] [v1] Thu, 22 Oct 2026 08:12:55 UTC (348 KB)

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Key points

  • Presents a scalable structural attack framework for multigraph neural networks using node-edge incidence matrices, adapting projected gradient descent and projected randomized block coordinate descent.
  • Introduces a cycle-based regularizer that penalizes changes in global counts of directed 2- and 3-cycles to measure attack plausibility.
  • Experiments on synthetic pattern-detection and realistic AML datasets show learned attacks substantially reduce PR-AUC, while adversarial training consistently improves robustness.
  • Code is available on GitHub; paper accepted at the Fifth Learning on Graphs Conference (LoG 2026).

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