3 papers
cs.LG2026
Unsupervised Graph Modeling for Anomaly Detection in Accounting Subject Relationships
Yuhan Wang, Ruobing Yan, Zhe Su +3
This paper addresses the problem of anomaly detection in accounting subject association structures, proposing a structured modeling and unsupervised discriminant framework based on…
cs.LG2025
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection
Zhong Li, Yuhang Wang, Matthijs van Leeuwen
Self-supervised learning (SSL) is an emerging paradigm that exploits supervisory signals generated from the data itself, and many recent studies have leveraged SSL to conduct graph…
cs.LG2024
Explainable Graph Neural Networks Under Fire
Zhong Li, Simon Geisler, Yuhang Wang +2
Predictions made by graph neural networks (GNNs) usually lack interpretability due to their complex computational behavior and the abstract nature of graphs. In an attempt to tackl…