arXiv:2603.07916v2 Announce Type: replace-cross Abstract: In recent advances, to enable a fully data-driven learning paradigm on relational databases (RDB), relational deep learning (RDL) is proposed to structure the RDB as a heterogeneous entity graph and adopt the graph neural network (GNN) as the predictive model. However, existing RDL methods neglect the imbalance problem of relational data in RDBs and risk under-representing the minority entities, leading to an unusable model in practice. In this work, we investigate, for the first time, class imbalance problem in RDB entity classificatio
The proliferation of relational deep learning, specifically in structuring relational databases as heterogeneous entity graphs, is highlighting practical limitations such as data imbalance that require immediate solutions.
Addressing the imbalance problem in relational deep learning is crucial for developing robust and usable AI models that can accurately represent minority entities in complex databases, impacting the reliability of data-driven insights.
This research introduces a novel approach to overcome a significant hurdle in relational deep learning, potentially enabling more equitable and accurate AI model performance across diverse datasets, making AI more practical for real-world database applications.
- · AI developers
- · Database management solutions
- · Data scientists
- · Industries relying on complex datasets
- · Legacy imbalanced RDL methods
- · Organizations with poor data governance
Improved accuracy and fairness of AI models in applications using relational databases.
Increased adoption of relational deep learning in critical sectors due to enhanced reliability and broader applicability.
The development of new AI-driven tools that can effectively leverage highly complex and previously unusable relational datasets, leading to novel insights and automation.
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