Grables: When Graph Structure Helps Tabular Prediction

Abstract:Simple row-wise predictors that score each row independently remain competitive with far more expressive architectures on tabular benchmarks, which are i.i.d. by construction. They cannot, however, represent natural targets on transactional, temporal, and relational tables, where labels depend on other rows. We show that row-wise prediction provably rules out targets driven by global counts, overlaps, and relational patterns. To make ``using structure'' precise across architectures, we introduce grables: a modular interface that separates how a table is lifted to a graph (constructor) from how predictions are computed on that graph (node predictor), placing gradient-boosted trees, neighbourhood aggregation, transformer-style tabular models, and relational GNNs on a single axis and pinpointing where expressive power comes from. The framework yields a hierarchy that answers when structure is needed: row-local models suffice for extension-invariant targets, lightweight neighbourhood aggregation captures shallow inter-row signals, and explicit graph construction is needed for compositional patterns that fixed aggregations cannot express without exponential feature blow-up. Experiments on synthetic, transactional, and relational data confirm the predicted separations, but also show that benchmark performance is a poor proxy for structural capability: row-local models score well on tasks they provably cannot represent, and a stress-test that suppresses row-local cues is needed to expose the gap. Conversely, access to cross-row context does not imply a model uses it. Feeding explicitly extracted inter-row structure to strong tabular learners yields gains exactly where the hierarchy predicts. The practical recipe is to add structure incrementally, and only where the target is extension-sensitive.

Machine Learning (cs.LG)

arXiv:2602.03945 [cs.LG]

(or arXiv:2602.03945v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2602.03945 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.