Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning

Problem
This paper addresses the limitations of existing Relational Deep Learning (RDL) methods that rely on fixed graph structures for relational databases (RDBs). The authors argue that the prevalent full-resolution property in graph construction often constrains the flexibility and adaptability of models, leading to suboptimal performance in relational prediction tasks. This work is presented as a preprint, indicating it has not yet undergone peer review.

Method
The authors introduce FROG (Full-Resolution and Optimizable Graph Structure Learning), a novel framework that reformulates relational structure learning as a learnable table role modeling problem. In this framework, tables are treated as both nodes and edges within a graph, facilitating a more dynamic representation of relational data. The core technical contributions include:

• Role-Driven Message Passing: This mechanism captures relational semantics by allowing the model to learn how different tables interact and contribute to the overall graph structure.
• Joint Optimization: FROG enables simultaneous optimization of both the graph structure and the GNN representations, enhancing the model’s ability to adapt to the underlying data.
• Functional Dependency Constraints: These constraints are introduced to maintain semantic consistency across table and entity levels, ensuring that the learned representations reflect the inherent relationships in the data.

The authors do not disclose specific details regarding the training compute or the datasets used, focusing instead on the methodological innovations.

Results
FROG demonstrates significant improvements over existing baselines on various relational prediction benchmarks. The paper reports that FROG achieves a performance increase of up to 15% in accuracy compared to state-of-the-art methods, such as traditional GNNs and fixed-structure RDL approaches. The results indicate that the role-driven message passing and the learnable graph structure contribute to enhanced predictive capabilities, particularly in complex relational scenarios.

Limitations
The authors acknowledge that FROG’s performance may be sensitive to the choice of hyperparameters, particularly those governing the role-driven message passing mechanism. They also note that while the framework shows promise, it may require substantial computational resources for large-scale RDBs, which could limit its applicability in resource-constrained environments. Additionally, the paper does not explore the scalability of the method across diverse relational datasets, which could be a critical factor for real-world applications.

Why it matters
This work has significant implications for the field of relational deep learning, as it challenges the conventional reliance on fixed graph structures and opens avenues for more flexible and adaptive modeling approaches. By framing relational structure learning as a learnable problem, FROG paves the way for future research to explore dynamic graph representations that can better capture the complexities of relational data. This could lead to improved performance in various applications, including knowledge graph completion, recommendation systems, and more complex relational reasoning tasks.

Authors: Yi Huang, Qingyun Sun, Jia Li, Xingcheng Fu, Jianxin Li
Source: arXiv:2605.21475
URL: https://arxiv.org/abs/2605.21475v1