We investigate whether structured knowledge retrieval from a mathematical library's dependency graph can improve neural theorem proving at inference time while maintaining explainability of the retrieved context. Through a controlled ablation study on the miniF2F-Test benchmark (197~tasks, 8~non-chain-of-thought modes, $K{=}8$ attempts per mode), we find that graph-based retrieval augmentation significantly improves proof generation on hard problems -- those where the base model's parametric knowledge is insufficient -- while having no measurable effect on problems the model already solves reliably. On 109~hard tasks, the best graph-augmented mode nearly triples the baseline success rate (pass@1: $3.56\% \to 10.42\%$, $+6.8$~percentage points, $p{=}0.001$, Wilcoxon signed-rank test). Deterministic pattern-based graph entry points outperform LLM-generated ones ($11\times$ faster, higher accuracy). The retrieved graph context is fully traceable: each hint maps to a specific edge in the Mathlib dependency graph, enabling the user to verify \emph{why} particular lemmas were suggested. The approach is training-free, composable with any base prover, and adds negligible computational overhead.