{"type":"ScholarlyArticle","title":"CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts","authors":[{"name":"Peiyuan Li","slug":"peiyuan-li","given":"Peiyuan","family":"Li","affiliation":"School of Computer Science and Technology , Tianjin University , Tianjin , China"},{"name":"Yongqi Huang","slug":"yongqi-huang","given":"Yongqi","family":"Huang","affiliation":"School of Computer Science and Technology , Tianjin University , Tianjin , China"},{"name":"Jitao Zhao","slug":"jitao-zhao","given":"Jitao","family":"Zhao","affiliation":"School of Computer Science and Technology , Tianjin University , Tianjin , China"},{"name":"Dongxiao He","slug":"dongxiao-he","given":"Dongxiao","family":"He","affiliation":"School of Computer Science and Technology , Tianjin University , Tianjin , China"},{"name":"Di Jin","slug":"di-jin","given":"Di","family":"Jin","affiliation":"School of Computer Science and Technology , Tianjin University , Tianjin , China"},{"name":"Weixiong Zhang","slug":"weixiong-zhang","given":"Weixiong","family":"Zhang","affiliation":"Department of Health Technology and Informatics , and Department of Data Science and Artificial Intelligence , The Hong Kong Polytechnic University , Kowloon , Hong Kong"}],"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 in 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 propose 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 views for downstream prediction. Extensive experiments show that CHoE consistently improves performance in few-shot cross-domain applications, outperforming all baseline approaches.","doi":"10.24963/ijcai.2026/309","url":"https://ijcai.org/proceedings/2026/309","pdf":"https://ijcai.org/proceedings/2026/0309.pdf","markdown":"https://ijcai.org/proceedings/2026/309.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/309","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":309,"pages":{"first":2780,"last":2788},"keywords":["Data Mining: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2025] Weilin Cai, Juyong Jiang, Fan Wang, Jing Tang, Sunghun Kim, and Jiayi Huang. 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