Reverse k-nearest neighbor (RkNN) search returns all data points that regard a query vector as one of their k-nearest neighbors (kNNs). Existing RkNN methods typically follow a filter-and-verification framework: vectors near the query vector are first collected as candidates and then verified against their kNN-radius (i.e., the distance to their k-th nearest neighbor). However, existing methods face two key limitations in high-dimensional spaces. First, nearby vectors often do not belong to the query's true RkNN set, resulting in excessive candidate expansion overhead. Second, existing methods compute kNN-radius online during verification, incurring substantial query-processing cost. To address these limitations, we propose HRNN, a hybrid graph index for approximate RkNN search. (1) Rather than directly treating nearby vectors as RkNN candidates, HRNN uses them as proxy points based on the assumption that a query's RkNN results can often be discovered through the RkNN results of its nearby vectors. (2) To reduce verification cost, HRNN materializes high-fidelity kNN-radius offline, eliminating expensive online reconstruction while preserving accuracy. HRNN combines a navigation graph, a ranked KNN graph, and reverse-neighbor lists into a hybrid index that supports efficient proxy retrieval, candidate generation, and kNN-radius access. We also develop efficient index construction and append-only maintenance algorithms. Extensive experiments show that HRNN consistently outperforms existing methods, achieving up to one order of magnitude higher throughput. Moreover, HRNN scales to datasets containing up to 10 million high-dimensional vectors while supporting efficient dynamic index maintenance.