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

TL;DR

This paper introduces a distributed GNN-based framework for exact subgraph matching that improves load balancing, cache efficiency, and query planning across multiple machines.

Contribution

It extends GNN-PE to distributed systems with novel load balancing, cache optimization, and query ranking techniques for scalable subgraph matching.

Findings

Achieves efficient distributed subgraph matching with load balancing and cache strategies.

Significantly improves matching efficiency and stability in multi-machine environments.

Utilizes METIS partitioning and online learning for optimized query execution.

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.