CrossHGL: A Text-Free Foundation Model for Cross-Domain Heterogeneous Graph Learning
Heterogeneous graph representation learning (HGRL) is essential for modeling complex systems with diverse node and edge types. However, most existing methods are limited to closed-world settings with shared schemas and feature spaces, hindering cross-domain generalization. While recent graph foundation models improve transferability, they often target homogeneous graphs, rely on domain-specific schemas, or require rich textual attributes. Consequently, text-free and few-shot cross-domain HGRL remains underexplored. To address this, we propose CrossHGL, a foundation framework that preserves and transfers multi-relational structural semantics without external textual supervision. Specifical...
| Authors | Xuanze Chen, Jiajun Zhou, Yadong Li et al. |
|---|---|
| Primary category | cs.LG |
| Published | 29 March 2026 |
| Last updated at source | 29 March 2026 |
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