PGformer: Fusing Kernelized Transformers and GCNs for Automated Proximity Graph Parameter Configuration

Approximate nearest neighbor search (ANNS) serves as the fundamental querying method in large-scale and high-dimensional vector datasets, for which proximity graph (PG)-based algorithms are the preferred solution, offering the best balance between query efficiency and accuracy. However, PG-based algorithms’ optimal performance across multiple indicators depends on extensive manual parameter configuration. To this end, we propose PGformer, an end-to-end framework that predicts performance to identify optimal graph configurations. PGformer integrates attention-driven global representation learning and neighbor-aware embedding extraction to capture comprehensive structural features. For large-scale scenarios, we implement a linear-complexity attention mechanism that maintains global accuracy while reducing computational overhead. These learned representations are then used to jointly estimate multiple performance metrics, facilitating precise candidate selection. The experimental results show that PGformer achieves competitive and often improved recommendation quality compared with traditional baselines, particularly in large-scale settings. In the evaluated scenarios, PGformer reduces recommendation time by about 40% and selects configurations that are close to the exhaustive-search reference optimum under recall-constrained objectives. Nevertheless, PGformer still relies on offline profiling data and a discretized candidate configuration space, which motivates further investigation under broader deployment environments and stronger distribution shifts.