How to Convert Relational Database Tables Into Graph Neural Network Inputs

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Developers building graph neural networks often start with relational data spread across tables, requiring a structured conversion process. The core rule is straightforward: tables with primary keys referenced by others become node types, while tables that link two keys become edge types. A critical and often silent bug involves node ID remapping, since graph libraries require contiguous integers starting from zero, not raw database keys like UUIDs or gapped auto-increments. High-cardinality categorical columns such as region should remain node features rather than separate node types, as promoting them creates hub nodes that blur distinct entity representations. Dangling foreign keys must also be pruned before conversion, as they can cause phantom nodes or crashes without clear error messages.

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