RelAD is a reconstruction-based framework for anomaly detection on relational data that combines conditional sparse-gated attribute reconstruction with dual-view multi-relational edge reconstruction and outperforms baselines on 6 constructed benchmarks.
Influence-oriented personalized federated learning.arXiv preprint arXiv:2410.03315, 2024
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Towards Anomaly Detection on Relational Data
RelAD is a reconstruction-based framework for anomaly detection on relational data that combines conditional sparse-gated attribute reconstruction with dual-view multi-relational edge reconstruction and outperforms baselines on 6 constructed benchmarks.