arXiv:2606. 18621v1 Announce Type: new Abstract: Relational databases are widely used for managing structured data in real-world systems.
By Shiyuan Li, Yunfeng Zhao, Yue Tan, Qingfeng Chen, Yixin Liu, Shirui Pan
Anomaly detection is often applied to data stored in relational databases, yet most existing methods require flattening multiple tables into a single feature matrix. This flattening can obscure entity...
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
The paper introduces an unsupervised hypergraph neural network designed to detect anomalous hyperedges—higher-order associations that deviate from typical patterns. Unlike conventional graph methods that capture only pairwise relationships, this approach leverages hypergraphs to model associations among any number of entities. Experiments on real-life datasets show the model effectively identifies unusual hyperedges without requiring labeled data.
By Md. Tanvir Alam, Md. Mahmudur Rahman, Md. Fahim Arefin, Chowdhury Farhan Ahmed, Zisan Mahmud, Md. Sadman Sakib, Carson K. Leung
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
FoundAna is a GNN‑assisted foundation model designed for graph anomaly detection across diverse datasets. It combines a GNN component with a transformer encoder enhanced by four positional encodings to capture both local and global structure, using reconstruction errors as anomaly scores. Experiments on nine benchmark datasets from financial, social, and citation networks show that FoundAna consistently outperforms state‑of‑the‑art baselines.
By Suprim Nakarmi, Chahana Dahal, Yue Zhao, Junggab Son, Zuobin Xiong