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arXiv · 2511.09052

Efficient Distributed Exact Subgraph Matching via GNN-PE: Load Balancing, Cache Optimization, and Query Plan Ranking

Yu Wang·Hui Wang·Jiake Ge·Xin Wang
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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.

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Yu Wang, Hui Wang, Jiake Ge, Xin Wang. 2025-11-12. Efficient Distributed Exact Subgraph Matching via GNN-PE: Load Balancing, Cache Optimization, and Query Plan Ranking. https://arxiv.org/abs/2511.09052

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