arXiv Machine Learning August 18, 2026 By Omair Shafi Ahmed, Zohair Shafi

Beat the Counter First: A Baseline for Temporal-Graph Anomaly Detectors

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

5d ago

Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detection

arXiv:2608. 12441v1 Announce Type: cross Abstract: Deep learning detectors for anomalies in dynamic graphs have reached strong accuracy, yet they remain opaque: when an edge is flagged, the analyst receives a score but no reason.

By Iyad Assaad Nekka, Hamida Seba, Khaled Walid Hidouci, Karima Amrouche

benchmarks

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.

By Simone Mungari, Albert Bifet, Giuseppe Manco, Bernhard Pfahringer

rag diffusion

arXiv Machine Learning

Jun 2

Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection

arXiv:2606. 00304v1 Announce Type: new Abstract: Graph anomaly detection methods aim to distinguish anomalous nodes.

By Yilin Liu, Hongchao Zhang, Taylor T. Johnson, Ahmad F. Taha, Meiyi Ma

benchmarks

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.

By Phan Nguyen, Dat Cao, Hien Chu, Khue Hoang

arXiv AI

Jun 2

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection

arXiv:2606. 01300v1 Announce Type: cross Abstract: Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry.

By Uzair Khan, Luigi Capogrosso, Francesco Biondani, Michele Magno, Franco Fummi, Francesco Setti, Marco Cristani

rag benchmarks

arXiv AI

Jul 2

PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection

arXiv:2602. 01359v3 Announce Type: replace-cross Abstract: Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transformers and foundation models, they incur high computational costs and memory usage, making them impractical for real-time and resource-constrained scenarios.

By Jinju Park, Seokho Kang

llms rag benchmarks

Related stories

Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detection

ARES: Anomaly Recognition Model For Edge Streams

Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection

A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection

PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection