CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
The article discusses a new method called CHoE for Cross-Domain Heterogeneous Graph Prompt Learning. This approach aims to enhance the performance of pre-trained models in diverse application domains. CHoE utilizes structure-conditioned experts and a prompt-based semantic fusion module to improve predictions in few-shot cross-domain scenarios.
- ▪Heterogeneous Graph Prompt Learning (HGPL) is designed to bridge the gap between pre-training models and their applications.
- ▪Current HGPL methods are limited to in-domain scenarios, which affects their performance in real-world applications.
- ▪CHoE introduces structure-conditioned experts and a routing mechanism to select compatible experts for each meta-path view.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15888 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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Computer Science > Machine Learning arXiv:2605.15888 (cs) [Submitted on 15 May 2026] Title:CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts Authors:Peiyuan Li, Yongqi Huang, Jitao Zhao, Dongxiao He, Di Jin, Weixiong Zhang View a PDF of the paper titled CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts, by Peiyuan Li and 5 other authors View PDF HTML (experimental) 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.
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