RAD: Rule-Augmented Relational Anomaly Detection
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 identity, schema structure, and multi-hop dependencies, limiting the detection of anomalies that depend on relational context rather than isolated feature values. Beyond preserving relational structure, relational anomaly detection raises an additional challenge: how to incorporate symbolic behavioral evidence into learned relational representations. To address these challenges, we study relation
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzyOverlapping authors or contributors · 62%ultralytics/yolov5 →
“Shared author/contributor keys: tran”
- LinkedLinked via arxiv author · 85%Noah Dahle →
“RAD: Rule-Augmented Relational Anomaly Detection”
- LinkedLinked via arxiv author · 85%Anne Tumlin →
“RAD: Rule-Augmented Relational Anomaly Detection”
- LinkedLinked via arxiv author · 85%Ngoc Tran →
“RAD: Rule-Augmented Relational Anomaly Detection”
- LinkedLinked via arxiv author · 85%Xenofon Koutsoukos →
“RAD: Rule-Augmented Relational Anomaly Detection”
- LinkedLinked via arxiv author · 85%Tyler Derr →
“RAD: Rule-Augmented Relational Anomaly Detection”