CrossHGL: A Text-Free Foundation Model for Cross-Domain Heterogeneous Graph Learning

Abstract

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. Specifically, a semantic-preserving transformation strategy homogenizes heterogeneous graphs while encoding interaction semantics into edge features. Based on this, a prompt-aware multi-domain pre-training framework with a Tri-Prompt mechanism captures transferable knowledge across feature, edge, and structure perspectives via self-supervised contrastive learning. For target-domain adaptation, we develop a parameter-efficient fine-tuning strategy that freezes the pre-trained backbone and performs few-shot classification via prompt composition and prototypical learning. Experiments on node-level and graph-level tasks show that CrossHGL consistently outperforms state-of-the-art baselines, yielding average relative improvements of 25.1% and 7.6% in Micro-F1 for node and graph classification, respectively, while remaining competitive in challenging feature-degenerated settings.

Figures (5)

• Figure 1: Challenges in cross-domain heterogeneous pre-training.
• Figure 2: Overview of the CrossHGL framework. The workflow consists of three phases: 1) Semantic-preserving graph transformation: each source and target heterogeneous graph is converted into a unified homogeneous graph through SVD-based feature alignment and meta-pattern mining, while heterogeneous semantics are preserved in semantics-enriched edge features; 2) Multi-domain semantic graph pre-training: a Tri-Prompt mechanism with feature, edge, and structure prompts is integrated with a shared GNN backbone to capture transferable knowledge from feature, relation, and topology perspectives through self-supervised contrastive learning; 3) Cross-domain semantic fine-tuning: the pre-trained backbone is frozen, source-domain prompts are composed with target-specific open prompts for few-shot adaptation, and the adapted representations are used for prototype-based prediction on target-domain instances.
• Figure 3: Impact of sample size ($k$) on ACM classification performance
• Figure 4: Performance comparison under different pre-training source settings. The full multi-source setting consistently achieves the best overall performance, while reducing the number of source domains leads to clear performance degradation on several target datasets.
• Figure 5: Comparison between direct flattening and our semantic-preserving graph transformation on GCN, GraphSAGE, and GAT under the 1-shot supervised setting, where the remaining nodes are split into validation and test sets following the protocol used in the main results.