CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910223701114880 |
|---|---|
| author | Li, Peiyuan Huang, Yongqi Zhao, Jitao He, Dongxiao Jin, Di Zhang, Weixiong |
| author_facet | Li, Peiyuan Huang, Yongqi Zhao, Jitao He, Dongxiao Jin, Di Zhang, Weixiong |
| contents | 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 settings, and their performance typically degrades when application domains shift. To address this serious limitation, we develop CHoE, a cross-domain HGPL method built upon an expert network. During pre-training, we introduce and train structure-conditioned experts, and during prompt tuning, we adopt a structure-aware expert routing and load balancing mechanism to select structurally compatible experts for each meta-path view. In addition, we design a prompt-based semantic fusion module to integrate representations across multiple views for downstream prediction. Extensive experiments show that CHoE consistently improves performance in few-shot cross-domain applications, outperforming all baseline approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15888 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts Li, Peiyuan Huang, Yongqi Zhao, Jitao He, Dongxiao Jin, Di Zhang, Weixiong Machine Learning Artificial Intelligence 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 settings, and their performance typically degrades when application domains shift. To address this serious limitation, we develop CHoE, a cross-domain HGPL method built upon an expert network. During pre-training, we introduce and train structure-conditioned experts, and during prompt tuning, we adopt a structure-aware expert routing and load balancing mechanism to select structurally compatible experts for each meta-path view. In addition, we design a prompt-based semantic fusion module to integrate representations across multiple views for downstream prediction. Extensive experiments show that CHoE consistently improves performance in few-shot cross-domain applications, outperforming all baseline approaches. |
| title | CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.15888 |