H$^2$GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs

• URL: http://arxiv.org/abs/2506.08298v2
• Date: Sun, 15 Jun 2025 02:48:38 GMT
• Title: H$^2$GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs
• Authors: Trung-Kien Nguyen, Heng Ping, Shixuan Li, Peiyu Zhang, Nikos Kanakaris, Nicholas Kotov, Paul Bogdan,
• Abstract summary: We introduce H$2$GFM, a novel framework designed to generalize across both HoTAGs and HeTAGs.<n>Our model projects diverse meta-relations among graphs under a unified textual space.<n>We employ a mixture of CGT experts to capture the heterogeneity in structural patterns among graph types.
• Score: 6.601515580215021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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