XDGNN: Efficient Distributed GNN Training via Explanation-Guided Subgraph Expansion

Jie Gao, Jia Hu, Geyong Min, Fei Hao

Abstract: Graph neural network (GNN) is a state-of-the-art technique for learning structural information from graph data. However, training GNNs on large-scale graphs is very challenging due to the size of real-world graphs and the message-passing architecture of GNNs. One promising approach for scaling GNNs is distributed training across multiple accelerators, where each accelerator holds a partitioned subgraph that fits in memory to train the model in parallel. Existing distributed GNN training methods require frequent and prohibitive embedding exchanges between partitions, leading to substantial communication overhead and limited the training efficiency. To address this challenge, we propose XDGNN, a novel distributed GNN training method that eliminates the forward communication bottleneck and thus accelerates training. Specifically, we design an explanation-guided subgraph expansion technique that incorporates important structures identified by eXplanation AI (XAI) methods into local partitions, mitigating information loss caused by graph partitioning. Then, XDGNN conducts communication-free distributed training on these self-contained partitions through training the model in parallel without communicating node embeddings in the forward phase. Extensive experiments demonstrate that XDGNN significantly improves training efficiency while maintaining the model accuracy compared with current distributed GNN training methods.

External IDs:dblp:journals/tpds/GaoHMH25