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This paper proposes RelShap, a framework that incorporates relational constraints and data provenance into Shapley value computation, making explanations more faithful to the data-generating process. It is estimator-agnostic and composes with existing SHAP estimators while exploiting functional dependencies to reduce runtime.
This paper introduces AutoGrable, a method that scores candidate graph constructions for tabular data without training a GNN, using a label-alignment risk based on 1-WL color refinement, enabling cheap search for effective table-to-graph mappings.
GRAB uses a GNN encoder to convert relational tables into latent tokens for frozen LLMs, achieving significant performance gains in multi-table question answering.
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.