MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training

Multi-domain graph pre-training is a crucial step in constructing foundational graph models with cross-domain generalization capabilities. However, existing methods predominantly rely on jointly training all source domain graphs, resulting in high computational costs. Furthermore, it remains unclear whether all source domain graph data contribute equally to effective transfer. This paper empirically reveals significant data redundancy in multi-domain graph pre-training. Based on this finding, we propose the Multi-domain Graph Pre-training Framework, MDGMIX, which combines boundary-aware subgraph mixing with hierarchical discrimination. By selecting boundary nodes to construct challenging mixed-domain subgraphs, MDGMIX employs coarse-grained domain discrimination and fine-grained domain decomposition losses to decouple shared patterns from domain-specific patterns. During adaptation, MDGMIX employs a lightweight prompt weighting mechanism to transfer source domain knowledge. Extensive experiments demonstrate that MDGMIX consistently outperforms strong baselines in few-shot classification tasks while exhibiting superior time and memory efficiency. The code is available at: https://github.com/zhengziyu77/MDGMIX

Graphs represent connected data such as social networks, papers, and biological systems. Existing methods for training general graph models often use all available datasets, which is expensive and may include redundant data. We find that much of this data is unnecessary and propose MDGMIX, a more efficient method that focuses on the most informative graph regions. MDGMIX helps the model learn both common patterns across domains and useful domain-specific knowledge. On few-label classification tasks, it achieves better accuracy while using less time and memory.