arXiv:2605.15888v2 Announce Type: replace Abstract: Heterogeneous Graph Prompt Learning (HGPL)has emerged as a promising paradigm for bridging the gap between the objectives of pre-training foundation models and their downstream applications in heterogeneous graph settings. However, existing HGPL methods are primarily designed for in-domain scenarios, whereas real-world deployments often span multiple domains, and the data used for pre-training and downstream tasks may originate from different distributions. Consequently, the applicability of current HGPL approaches is limited to in-domain set
The proliferation of AI foundation models and their diverse applications across various domains necessitates robust methods for cross-domain adaptation, which this paper addresses by proposing a new technique for Heterogeneous Graph Prompt Learning.
Improving AI model generalization and adaptability across different data distributions and applications will accelerate AI deployment and reduce the cost of specialized model development.
Current AI models, particularly in graph learning, are often limited to in-domain scenarios; this research suggests a path towards more flexible and transferable models capable of handling real-world, multi-domain heterogeneity.
- · AI model developers
- · Organizations with diverse data types
- · Graph AI applications
- · Single-domain AI solution providers
Cross-domain AI model performance will improve, enabling more versatile applications.
Reduced need for extensive re-training or fine-tuning of AI models when deployed in new, related domains.
Accelerated development of AI agents capable of operating effectively across disparate data environments.
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Read at arXiv cs.LG