arXiv AI August 12, 2026 By Ziyan Wang, Liwen Wu, Cheng Xie, Song Gao, Zhenli He, Xin Jin

ProTAGAD: A Foundation Model for TAG Anomaly Detection with Decoupled Topological and Textual Prototypes

arXiv:2608. 10699v1 Announce Type: cross Abstract: Text-Attributed Graphs (TAGs), endowed with abundant textual content along with topological structures, have emerged as a versatile backbone for real-world anomaly detection spanning large language model security, social network moderation, and cyber threat identification.

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

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.

By Laura Jiang, Reza Ryan, Qian Li, Nasim Ferdosian

benchmarks

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.

By Yuxing Tian, Yiyan Qi, Fengran Mo, Weixu Zhang, Jian Guo, Jian-Yun Nie

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 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 17

Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection

arXiv:2605. 29526v2 Announce Type: replace-cross Abstract: Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors.

By Runang He, Tongya Zheng, Huiling Peng, Yuanyu Wan, Bingde Hu, Jiawei Chen, Canghong Jin, Mingli Song, Can Wang

benchmarks safety

arXiv Machine Learning

Jun 9

Normality Calibration in Semi-supervised Graph Anomaly Detection

arXiv:2510. 02014v3 Announce Type: replace Abstract: Graph anomaly detection (GAD) has attracted growing interest for its crucial ability to uncover irregular patterns in broad applications.

By Guolei Zeng, Hezhe Qiao, Guoguo Ai, Jinsong Guo, Guansong Pang

safety

Related stories

A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection

Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision

ARES: Anomaly Recognition Model For Edge Streams

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

Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection

Normality Calibration in Semi-supervised Graph Anomaly Detection