AI RESEARCH
A Systematic Evaluation Protocol of Graph-Derived Signals for Tabular Machine Learning
arXiv CS.AI
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ArXi:2603.13998v1 Announce Type: new While graph-derived signals are widely used in tabular learning, existing studies typically rely on limited experimental setups and average performance comparisons, leaving the statistical reliability and robustness of observed gains largely unexplored. Consequently, it remains unclear which signals provide consistent and robust improvements. This paper presents a taxonomy-driven empirical analysis of graph-derived signals for tabular machine learning.