统一处理同质与异质文本图，提升图基础模型泛化能力
H$^2$GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs
- 通过统一文本空间建模跨图元关系，融合上下文语义
- 在多种同质/异质图上实现更优节点表征，提升下游任务性能
- 适合需要跨图类型泛化的图学习研究者和应用开发者
图学习在多个领域的广泛应用推动了图基础模型（GFM）的发展，旨在实现跨图与任务的通用性。现有研究主要基于具有文本属性的同质图（HoTAGs）来应对图间节点特征的异质性，但对包含多种节点/边类型的异质图（HeTAGs）探索不足。为此，本文提出H²GFM框架，可统一处理HoTAGs与HeTAGs。模型将不同图中的元关系投影至统一文本空间，并利用上下文编码捕捉空间与高阶语义关系。为获得鲁棒节点表示，提出上下文自适应图变压器（CGT），有效整合上下文邻居及其关系信息；同时引入多专家CGT结构以捕获不同图类型间的结构模式异质性。在广泛范围的HoTAGs与HeTAGs及学习场景上的实验验证了模型的有效性。
原文摘要 · Abstract (English)
The growing interests and applications of graph learning in diverse domains have propelled the development of a unified model generalizing well across different graphs and tasks, known as the Graph Foundation Model (GFM). Existing research has leveraged text-attributed graphs (TAGs) to tackle the heterogeneity in node features among graphs. However, they primarily focus on homogeneous TAGs (HoTAGs), leaving heterogeneous TAGs (HeTAGs), where multiple types of nodes/edges reside, underexplored. To enhance the capabilities and applications of GFM, we introduce H$^2$GFM, a novel framework designed to generalize across both HoTAGs and HeTAGs. Our model projects diverse meta-relations among graphs under a unified textual space, and employs a context encoding to capture spatial and higher-order semantic relationships. To achieve robust node representations, we propose a novel context-adaptive graph transformer (CGT), effectively capturing information from both context neighbors and their relationships. Furthermore, we employ a mixture of CGT experts to capture the heterogeneity in structural patterns among graph types. Comprehensive experiments on a wide range of HoTAGs and HeTAGs as well as learning scenarios demonstrate the effectiveness of our model.
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