通过拓扑对齐提升跨域图模型的鲁棒知识迁移能力。
Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment
- 通过自适应平衡特征与拓扑，对齐不同领域图结构。
- 在异质与同质图数据集上均实现更优的跨域迁移性能。
- 适合需要鲁棒图学习的工业场景和对抗性环境。
近期计算机视觉与自然语言处理的进步促使研究者尝试在多领域上预训练通用图基础模型。然而，不同领域间图拓扑差异显著，且真实世界图常呈稀疏状，存在噪声连接与对抗攻击风险。为此，本文提出多域图基础模型（MDGFM），一种统一框架，通过对齐并利用跨域拓扑信息，实现稳健的知识迁移。MDGFM通过自适应平衡特征与拓扑，优化原始图结构以消除噪声并对齐拓扑形态。为进一步增强知识迁移，引入高效提示调优方法。通过对齐拓扑，MDGFM不仅提升了多域预训练效果，还实现了对未见领域的稳健知识迁移。理论分析证明了其有效性与域泛化能力。在同质与异质图数据集上的大量实验验证了该方法的鲁棒性与有效性。
原文摘要 · Abstract (English)
Recent advances in CV and NLP have inspired researchers to develop general-purpose graph foundation models through pre-training across diverse domains. However, a fundamental challenge arises from the substantial differences in graph topologies across domains. Additionally, real-world graphs are often sparse and prone to noisy connections and adversarial attacks. To address these issues, we propose the Multi-Domain Graph Foundation Model (MDGFM), a unified framework that aligns and leverages cross-domain topological information to facilitate robust knowledge transfer. MDGFM bridges different domains by adaptively balancing features and topology while refining original graphs to eliminate noise and align topological structures. To further enhance knowledge transfer, we introduce an efficient prompt-tuning approach. By aligning topologies, MDGFM not only improves multi-domain pre-training but also enables robust knowledge transfer to unseen domains. Theoretical analyses provide guarantees of MDGFM's effectiveness and domain generalization capabilities. Extensive experiments on both homophilic and heterophilic graph datasets validate the robustness and efficacy of our method.
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