{"type":"ScholarlyArticle","title":"Robust Contrastive Graph Clustering with Adaptive Local-Global Integration","authors":[{"name":"Lei Zhang","slug":"lei-zhang","given":"Lei","family":"Zhang","affiliation":""},{"name":"Fubo Sun","slug":"fubo-sun","given":"Fubo","family":"Sun","affiliation":""},{"name":"Haipeng Yang","slug":"haipeng-yang","given":"Haipeng","family":"Yang","affiliation":""},{"name":"Zhong Guan","slug":"zhong-guan","given":"Zhong","family":"Guan","affiliation":""},{"name":"Likang Wu","slug":"likang-wu","given":"Likang","family":"Wu","affiliation":""}],"abstract":"Graph clustering is essential in graph analysis for revealing structural patterns and node communities. Despite recent advances in self-supervised contrastive learning that have improved clustering via structural and attribute signals, existing methods still struggle to flexibly capture high-order local structures and often overlook global semantics in complex graphs. These limitations lead to suboptimal node representations, especially in real-world graphs with fragmented structures and ambiguous cluster boundaries. To address these limitations, a contrastive graph clustering framework is proposed to jointly integrate multi-scale local structures with global semantics via attention mechanisms. At the local level, GNN-based topological signals extracted from multiple propagation depths are adaptively fused through attention-based weighting to capture multi-scale neighborhood features. At the global level, semantic prototypes derived from dynamically evolving cluster centers are adaptively aggregated through attention to guide node representations and enhance inter-cluster separability. The model is trained under a dual-view contrastive learning paradigm with a hybrid objective that combines instance-level and structure-aware losses to improve representation robustness and discrimination. Experiments on eight real-world graph datasets demonstrate that our method achieves competitive clustering performance. Code is  available at https://github.com/vege12138/w2.","doi":"10.24963/ijcai.2026/357","url":"https://ijcai.org/proceedings/2026/357","pdf":"https://ijcai.org/proceedings/2026/0357.pdf","markdown":"https://ijcai.org/proceedings/2026/357.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/357","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":357,"pages":{"first":3214,"last":3222},"keywords":["Machine Learning: Multi-view learning","Data Mining: Mining text, web, social media","Data Mining: Mining graphs"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}