{"ID":2842658,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09052","arxiv_id":"2511.09052","title":"Efficient Distributed Exact Subgraph Matching via GNN-PE: Load Balancing, Cache Optimization, and Query Plan Ranking","abstract":"Exact subgraph matching on large-scale graphs remains a challenging problem due to high computational complexity and distributed system constraints. Existing GNN-based path embedding (GNN-PE) frameworks achieve efficient exact matching on single machines but lack scalability and optimization for distributed environments. To address this gap, we propose three core innovations to extend GNN-PE to distributed systems: (1) a lightweight dynamic correlation-aware load balancing and hot migration mechanism that fuses multi-dimensional metrics (CPU, communication, memory) and guarantees index consistency; (2) an online incremental learning-based multi-GPU collaborative dynamic caching strategy with heterogeneous GPU adaptation and graph-structure-aware replacement; (3) a query plan ranking method driven by dominance embedding pruning potential (PE-score) that optimizes execution order. Through METIS partitioning, parallel offline preprocessing, and lightweight metadata management, our approach achieves \"minimum edge cut + load balancing + non-interruptible queries\" in distributed scenarios (tens of machines), significantly improving the efficiency and stability of distributed subgraph matching.","short_abstract":"Exact subgraph matching on large-scale graphs remains a challenging problem due to high computational complexity and distributed system constraints. Existing GNN-based path embedding (GNN-PE) frameworks achieve efficient exact matching on single machines but lack scalability and optimization for distributed environment...","url_abs":"https://arxiv.org/abs/2511.09052","url_pdf":"https://arxiv.org/pdf/2511.09052v2","authors":"[\"Yu Wang\",\"Hui Wang\",\"Jiake Ge\",\"Xin Wang\"]","published":"2025-11-12T07:06:33Z","proceeding":"cs.DB","tasks":"[\"cs.DB\"]","methods":"[\"Graph Neural Network\"]","has_code":false}