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.
Cite
@article{arxiv.2511.09052,
title = {Efficient Distributed Exact Subgraph Matching via GNN-PE: Load Balancing, Cache Optimization, and Query Plan Ranking},
author = {Yu Wang and Hui Wang and Jiake Ge and Xin Wang},
journal= {arXiv preprint arXiv:2511.09052},
year = {2026}
}
Comments
We request the withdrawal of this paper. After in-depth analysis and comparison with the latest research in the field, it is found that the research method adopted in this paper is outdated. We take this withdrawal seriously to maintain the rigor of academic research and avoid misleading subsequent researchers in the field