The strongest technical case is structural alignment. Graph traversal directly expresses variable-length paths, many-to-many links, and typed relationships; graph neural methods can then aggregate information across those relations. Yet performance is workload-dependent. Partition placement, traversal depth, indexing, cardinality, and query selectivity can dominate the outcome. Relational optimizers remain highly effective for fixed-schema aggregation and set-oriented workloads. The defensible conclusion is not universal superiority, but a better fit for relationship-first questions.