{"type":"ScholarlyArticle","title":"Bridging Feature-structural Homophily and Long-range Heterogeneity for Self-supervised Heterogeneous Graph Learning","authors":[{"name":"Minda Chen","slug":"minda-chen","given":"Minda","family":"Chen","affiliation":""},{"name":"Yujie Mo","slug":"yujie-mo","given":"Yujie","family":"Mo","affiliation":""},{"name":"Junkai Huang","slug":"junkai-huang","given":"Junkai","family":"Huang","affiliation":""},{"name":"Guoqiu Wen","slug":"guoqiu-wen","given":"Guoqiu","family":"Wen","affiliation":""},{"name":"Xiaofeng Zhu","slug":"xiaofeng-zhu","given":"Xiaofeng","family":"Zhu","affiliation":""}],"abstract":"Self-supervised heterogeneous graph learning has achieved promising results in diverse applications but still faces two issues: (i) existing methods focus on either feature similarity or meta-path to capture homophily, neglecting their inherent complementarity; (ii) existing methods rely on meta-paths to capture interactions among same-type nodes, which may introduce noise and inherently exclude long-range cross-type interactions. To address these issues, we first propose a self-expressive solver that captures the complementary homophily between meta-paths and node features to obtain homophilous representations. Meanwhile, we design separate path encoders to model diverse interactions, thus explicitly including cross-type interactions while mitigating noise via adaptive fusion. Theoretical analysis verifies that homophilous representations exhibit a high-order grouping effect to capture complementary homophily, while path encoders possess adaptive smoothness capabilities to filter noise. Extensive experiments on diverse datasets, including a large-scale dataset, demonstrate the superiority of the proposed method.","doi":"10.24963/ijcai.2026/280","url":"https://ijcai.org/proceedings/2026/280","pdf":"https://ijcai.org/proceedings/2026/0280.pdf","markdown":"https://ijcai.org/proceedings/2026/280.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/280","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":280,"pages":{"first":2519,"last":2527},"keywords":["Data Mining: Mining graphs","Machine Learning: Self-supervised Learning","Data Mining: Mining heterogenous data","Machine Learning: Representation learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}