Google Research: Graph foundation models for relational data
10 months ago
- #Relational Data
- #Graph Learning
- #Machine Learning
- Relational databases are key for enterprise data and prediction services.
- Traditional ML methods struggle with relational schema connectivity.
- Graph Neural Networks (GNNs) are fixed to specific graphs and lack generalization.
- Graph Foundation Models (GFM) aim to generalize across relational data.
- Relational tables can be transformed into heterogeneous graphs for ML.
- GFMs require transferable methods for encoding arbitrary database schemas.
- GFMs show significant performance boosts over single-table baselines.
- GFMs improve zero-shot and few-shot generalization in ML tasks.