Towards Anomaly Detection on Relational Data

• URL: http://arxiv.org/abs/2606.18621v1
• Date: Wed, 17 Jun 2026 02:36:32 GMT
• Title: Towards Anomaly Detection on Relational Data
• Abstract summary: We propose a reconstruction-based framework that captures anomalies from both attribute and relational edge reconstruction.<n>RelAD consistently outperforms other baselines while achieving competitive efficiency.
• Score: 51.473064583839296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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; and 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 entity 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 other baselines while achieving competitive efficiency.

Related papers

• RAD: Rule-Augmented Relational Anomaly Detection [11.251069238986483]
  Anomaly detection is often applied to data stored in relational databases.<n>Most existing methods require flattening multiple tables into a single feature matrix.<n>We propose RAD, a rule-augmented relational anomaly detector.
  arXiv  Detail & Related papers  (2026-08-24T16:36:17Z)
• CORE: In-Context Reconstruction for Unified Tabular Anomaly Detection [50.334454857423395]
  An emerging trend towards unified anomaly detection (TAD) seeks to detect anomalies across different datasets using a single generalizable model.<n>We propose an in-COntext REconstruction approach for unified TAD (CORE for short)<n>CORE formulates unified TAD as an in-context reconstruction problem, eliminating the need for labeled or synthesized anomalies.
  arXiv  Detail & Related papers  (2026-07-30T03:08:49Z)
• uLEAD-TabPFN: Uncertainty-aware Dependency-based Anomaly Detection with TabPFN [26.03194873655899]
  We propose uLEAD-TabPFN, a dependency-based anomaly detection framework built on Prior-Data Fitted Networks (PFNs)<n>i.e., it identifies anomalies as violations of conditional dependencies in a learned latent space, leveraging frozen PFNs for dependency estimation.<n>i.e., it achieves strong performance in medium- and high-dimensional settings, where it attains the top average rank.
  arXiv  Detail & Related papers  (2026-04-22T07:04:08Z)
• Towards One-for-All Anomaly Detection for Tabular Data [87.63505963517512]
  Tabular anomaly detection (TAD) is critical in many real-world applications.<n>We propose OFA-TAD, a framework that only requires one-time training on multiple source datasets.<n>Experiments on 34 datasets from 14 domains demonstrate that OFA-TAD achieves superior anomaly detection performance.
  arXiv  Detail & Related papers  (2026-03-15T14:42:05Z)
• ARC: A Generalist Graph Anomaly Detector with In-Context Learning [62.202323209244]
  ARC is a generalist GAD approach that enables a one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly.<n> equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset.<n>Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC.
  arXiv  Detail & Related papers  (2024-05-27T02:42:33Z)
• RESTAD: REconstruction and Similarity based Transformer for time series Anomaly Detection [3.0377067713090633]
  Anomaly detection in time series data is crucial across various domains. We introduce RESTAD, an adaptation of the Transformer model by incorporating a layer of Radial Basis Function (RBF) neurons within its architecture.
  arXiv  Detail & Related papers  (2024-05-13T07:10:35Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks [35.93516937521393]
  This paper proposes a self-supervised learning framework that jointly optimize a multi-view contrastive learning-based module and an attribute reconstruction-based module to more accurately detect anomalies on attributed networks. Experiments conducted on five benchmark datasets show our model outperforms current state-of-the-art models.
  arXiv  Detail & Related papers  (2022-05-10T11:35:32Z)
• CSCAD: Correlation Structure-based Collective Anomaly Detection in Complex System [11.739889613196619]
  We propose a correlation structure-based collective anomaly detection model for high-dimensional anomaly detection problem in large systems. Our framework utilize graph convolutional network combining a variational autoencoder to jointly exploit the feature space correlation and reconstruction deficiency of samples. An anomaly discriminating network can then be trained using low anomalous degree samples as positive samples, and high anomalous degree samples as negative samples.
  arXiv  Detail & Related papers  (2021-05-30T09:28:25Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.