arXiv Machine Learning
Jun 18
Towards Anomaly Detection on Relational Data
arXiv:2606. 18621v1 Announce Type: new Abstract: Relational databases are widely used for managing structured data in real-world systems.
Towards Anomaly Detection on Relational Data
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.
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arXiv AI
Jun 12
A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction
arXiv:2606. 12673v1 Announce Type: cross Abstract: Cross-domain graph anomaly detection (GAD) aims to identify abnormal nodes in unseen target graphs, showing strong potential in real-world applications with heterogeneous graph data.
arXiv Machine Learning
Jun 9
A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection
arXiv:2510. 26307v3 Announce Type: replace-cross Abstract: Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essential for ensuring system resilience.
arXiv AI
Jul 22
Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios
arXiv:2607. 18289v1 Announce Type: cross Abstract: Continual anomaly detection (CAD) studies how models can adapt to evolving data distributions while retaining performance on previously observed regimes.
arXiv AI
Jun 2
Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision
arXiv:2602. 20019v2 Announce Type: replace-cross Abstract: Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies.
arXiv Machine Learning
Jul 31
ARES: Anomaly Recognition Model For Edge Streams
arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.
arXiv AI
1d ago
FinFraudBench: A Heterogeneous Graph Benchmark for Financial Fraud Detection
arXiv:2608. 15177v1 Announce Type: cross Abstract: The increasing complexity of digital financial systems has reshaped financial fraud detection from isolated transaction classification into relational risk reasoning over interconnected financial entities.
Hugging Face Trending Papers
Jun 29
FlexTab: A Flexible Encoder-Decoder Architecture for In-Context Learning Across Diverse Tabular Tasks
We introduce FlexTab, a flexible encoder-decoder architecture for in-context learning on tabular data that pairs a single, task-agnostic encoder with a suite of task-specific decoders. Unlike existing tabular in-context learners, which entangle feature representations with a specific prediction target, our design produces \textit{target-agnostic} row embeddings that can be leveraged across a wide range of downstream tasks within a table-native in-context learning setup.
arXiv AI
Jul 24
CLOE: Christoffel Loss Autoencoder for Anomaly Detection
arXiv:2607. 20530v1 Announce Type: cross Abstract: Semi-supervised anomaly detection plays a key role in diverse fields such as process monitoring, healthcare, and finance.
arXiv Machine Learning
Jun 16
MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection
arXiv:2602. 09329v3 Announce Type: replace Abstract: Quality benchmarks are essential for fairly and accurately tracking scientific progress and enabling practitioners to make informed methodological choices.
arXiv Machine Learning
Jun 10
AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning
arXiv:2602. 13807v2 Announce Type: replace Abstract: Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under complex settings.
arXiv Machine Learning
1d ago
Beat the Counter First: A Baseline for Temporal-Graph Anomaly Detectors
arXiv:2608. 15965v1 Announce Type: new Abstract: Progress in streaming, edge-level graph anomaly detection (GAD) has been marked by increasingly elaborate architectures, from count-min-sketch chi square tests to memory-augmented attention networks.