mlsanityData to 7 Oct 2026

Towards Anomaly Detection on Relational Data

arXiv 2606.18621 ↗First posted 17 Jun 2026cs.LGcited by 4

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Abstract (arXiv)

Relational databases are widely used for managing structured data in real-world systems. Detecting anomalies from such relational data is crucial for identifying fraud, risks, and abnormal behaviors, yet remains under-explored. The key challenges lie in the intrinsic complexity of relational data: multi-table attributes are high-dimensional and heterogeneous, making sparse abnormal clues easy to overwhelm by normal or irrelevant information. Moreover, anomalies may further manifest as abnormal connection patterns across different foreign-key relations, which existing tabular and graph anomaly detection methods are ill-suited to capture. To address them, we propose RelAD, a reconstruction-based framework that captures anomalies from both attribute and relational edge reconstruction. RelAD contains two core modules: conditional sparse-gated attribute reconstruction, which suppresses redundant multi-table attributes and emphasizes abnormal semantic blocks, and dual-view multi-relational edge reconstruction, which detects relation-specific abnormal connections from both intrinsic and behavioral instance profiles. The resulting attribute and relational signals are integrated through a lightweight fusion module to produce the final anomaly score. We further construct 6 benchmark datasets with systematic anomalies, on which extensive experiments show that RelAD consistently outperforms baselines while achieving competitive efficiency..The source code is available at https://github.com/Shiy-Li/RelAD.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 43 of its 58 references that are in the map. First posted on arXiv 17 Jun 2026.

Builds on

43 of 58 references.

References · 43

  1. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  2. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  3. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  4. 2021Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learningcited by 175
  5. 2024RelBench: A Benchmark for Deep Learning on Relational Databasescited by 90
  6. 2018Generative Adversarial Active Learning for Unsupervised Outlier Detectioncited by 65
  7. 2025Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generationcited by 57
  8. 2023Relational Deep Learning: Graph Representation Learning on Relational Databasescited by 55
  9. 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learningcited by 46
  10. 2025RelGNN: Composite Message Passing for Relational Deep Learningcited by 45
  11. 2023Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detectioncited by 43
  12. 2021LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networkscited by 42
31 more
  1. 2025Griffin: Towards a Graph-Centric Relational Database Foundation Modelcited by 41
  2. 2024Deep Graph Anomaly Detection: A Survey and New Perspectivescited by 40
  3. 2025BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attackscited by 37
  4. 2025Relational Transformer: Toward Zero-Shot Foundation Models for Relational Datacited by 32
  5. 2025Relational Graph Transformercited by 30
  6. 2023PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detectioncited by 25
  7. 2025A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detectioncited by 24
  8. 2025Large Language Models are Good Relational Learnerscited by 22
  9. 2025Relational Deep Learning: Challenges, Foundations and Next-Generation Architecturescited by 22
  10. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  11. 2026RelBench v2: A Large-Scale Benchmark and Repository for Relational Datacited by 17
  12. 2022Data-Efficient and Interpretable Tabular Anomaly Detectioncited by 15
  13. 2026PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Modelscited by 15
  14. 2023Beyond Individual Input for Deep Anomaly Detection on Tabular Datacited by 14
  15. 2025Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approachcited by 14
  16. 2025A Survey of Generalization of Graph Anomaly Detection: From Transfer Learning to Foundation Modelscited by 13
  17. 2026DynHD: Hallucination Detection for Diffusion Large Language Models via Denoising Dynamics Deviation Learningcited by 13
  18. 2025SALT: Sales Autocompletion Linked Business Tables Datasetcited by 12
  19. 2025Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Timecited by 12
  20. 2026OFA-MAS: One-for-All Multi-Agent System Topology Design based on Mixture-of-Experts Graph Generative Modelscited by 12
  21. 2024SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detectioncited by 11
  22. 2025FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detectioncited by 10
  23. 2025Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modelingcited by 9
  24. 2026From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detectioncited by 8
  25. 2024Retrieval Augmented Deep Anomaly Detection for Tabular Datacited by 6
  26. 2024Influence-Oriented Personalized Federated Learningcited by 6
  27. 2026FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detectioncited by 6
  28. 2026Towards One-for-All Anomaly Detection for Tabular Datacited by 5
  29. 2024Disentangling Tabular Data Towards Better One-Class Anomaly Detectioncited by 4
  30. 2026Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approachcited by 4
  31. 2026CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detectioncited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026DeMTS: Denoising Trajectories as Multivariate Time Series for Hallucination Detection in Diffusion Language Modelscited by 0
  2. 2026SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detectioncited by 0
  3. 2026HOPE: Heterophily-Aware Open-Set Node Classification with Pseudo-Extrapolationcited by 0
  4. 2026DiFA: Dual Evidence Fusion and Aggregation for Token-Level Text Anomaly Detectioncited by 0