A Locality Radius Framework for Understanding Relational Inductive Bias in Database Learning
arXiv:2602.17092v1 Announce Type: new Abstract: Foreign key discovery and related schema-level prediction tasks are often modeled using graph neural networks (GNNs), implicitly assuming that relational inductive bias improves performance. However, it remains unclear when multi-hop structural reasoning is actually necessary. In this work, we introduce locality radius, a formal measure of the minimum structural neighborhood required to determine a prediction in relational schemas. We hypothesize that model performance depends critically on alignment between task locality radius and architectural aggregation depth. We conduct a controlled empirical study across foreign key prediction, join cost estimation, blast radius regression, cascade impact classification, and additional graph-derived schema tasks. Our evaluation includes multi-seed experiments, capacity-matched comparisons, statistical significance testing, scaling analysis, and synthetic radius-controlled benchmarks. Results revea
arXiv:2602.17092v1 Announce Type: new Abstract: Foreign key discovery and related schema-level prediction tasks are often modeled using graph neural networks (GNNs), implicitly assuming that relational inductive bias improves performance. However, it remains unclear when multi-hop structural reasoning is actually necessary. In this work, we introduce locality radius, a formal measure of the minimum structural neighborhood required to determine a prediction in relational schemas. We hypothesize that model performance depends critically on alignment between task locality radius and architectural aggregation depth. We conduct a controlled empirical study across foreign key prediction, join cost estimation, blast radius regression, cascade impact classification, and additional graph-derived schema tasks. Our evaluation includes multi-seed experiments, capacity-matched comparisons, statistical significance testing, scaling analysis, and synthetic radius-controlled benchmarks. Results reveal a consistent bias-radius alignment effect.
Executive Summary
This article introduces the concept of locality radius, a measure of the minimum structural neighborhood required for prediction in relational schemas. The authors hypothesize that model performance is dependent on the alignment between task locality radius and architectural aggregation depth. Through a controlled empirical study, the results reveal a consistent bias-radius alignment effect, providing insights into the role of relational inductive bias in database learning. The study has implications for the design of graph neural networks and related schema-level prediction tasks.
Key Points
- ▸ Introduction of locality radius concept
- ▸ Hypothesis of bias-radius alignment effect
- ▸ Empirical study across multiple tasks
Merits
Methodological Rigor
The study employs a range of methodologies, including multi-seed experiments and statistical significance testing, to establish the validity of the findings.
Demerits
Limited Generalizability
The study focuses on specific tasks and datasets, which may limit the generalizability of the results to other domains and applications.
Expert Commentary
The introduction of the locality radius concept provides a valuable framework for understanding the role of relational inductive bias in database learning. The empirical study provides strong evidence for the bias-radius alignment effect, with implications for the design of graph neural networks and related schema-level prediction tasks. However, further research is needed to establish the generalizability of the findings and to explore the applications of the locality radius concept in other domains.
Recommendations
- ✓ Further research on the generalizability of the findings
- ✓ Exploration of the applications of the locality radius concept in other domains
