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This paper introduces RelAD, a reconstruction-based framework for detecting anomalies in relational databases by jointly modeling attribute and relational edge reconstruction. Extensive experiments on six new benchmarks show RelAD outperforms existing methods.
This paper introduces RelGT-AC, a relational graph transformer architecture tailored for autocomplete tasks in relational databases. The model extends the RelGT architecture with column masking to prevent trivial solutions, a unified task head for multiple prediction types, and a TF-IDF text encoder to leverage lexical signals, achieving significant improvements over baselines on RelBench v2 benchmarks.
This paper explores the expressive power of Deep Homomorphism Networks (DHNs) for learning over relational databases, linking them to fragments of first-order logic and SQL, and analyzing static analysis problems like emptiness and subsumption.