MetaSieve: Faster Relational Deep Learning through SQL-Based Metapath Selection
Relational Deep Learning (RDL) is an effective approach to machine learning over multi-table relational databases. In RDL, a database is modeled as a graph in which each row is a node and each foreign-key relation is an edge, and a graph neural network (GNN) is trained on this graph. Training a GNN requires sampling a subgraph around every seed node in the training set, and the cost of training is largely determined by the size of these subgraphs. This paper aims to reduce subgraph size by leveraging the join and aggregation capabilities of relational database systems. We observe that sampled subgraphs are obtained by following metapaths composed of foreign-key links, and that many of the...
| Authors | Fahim Shahriar Khan, Ashraf Aboulnaga |
|---|---|
| Primary category | cs.DB |
| Published | 26 August 2026 |
| Last updated at source | 26 August 2026 |
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