{"type":"ScholarlyArticle","title":"Beyond Implicit Constraint: Explicit Low-Rank Structured Subspace Learning for Fast Attributed Graph Clustering","authors":[{"name":"Yaoming Cai","slug":"yaoming-cai","given":"Yaoming","family":"Cai","affiliation":""},{"name":"Song Liu","slug":"song-liu","given":"Song","family":"Liu","affiliation":""},{"name":"Zijia Zhang","slug":"zijia-zhang","given":"Zijia","family":"Zhang","affiliation":""},{"name":"You Wu","slug":"you-wu","given":"You","family":"Wu","affiliation":""},{"name":"Xiaobo Liu","slug":"xiaobo-liu","given":"Xiaobo","family":"Liu","affiliation":""},{"name":"Yao Ding","slug":"yao-ding","given":"Yao","family":"Ding","affiliation":""},{"name":"Fei Li","slug":"fei-li","given":"Fei","family":"Li","affiliation":""}],"abstract":"Attributed graph clustering has achieved remarkable success by synergistically integrating topological structures and node attributes. While subspace learning has emerged as a dominant paradigm for node partitioning, most existing methods rely on implicit low-rank constraints, which often fail to capture complex nonlinear manifolds and suffer from prohibitive computational overhead on large-scale graphs. In this paper, we propose ELSS (Explicit Low-rank Structured Subspace learning), a scalable and robust framework that transcends implicit formulations. Specifically, ELSS learns an explicit and nonlinear low-rank subspace within a graph-structured embedding space, effectively uncovering latent cluster structures. To effectively mitigate the pervasive oversmoothing issue, we introduce a homophily-aware adaptive graph filter, which dynamically calibrates smoothing intensity to preserve discriminative ego-information. Furthermore, to ensure linear scalability, we develop a PageRank-guided structural sampling strategy for anchor-based approximation, which identifies pivotal landmarks based on their global topological prestige. Theoretical analysis guarantees that ELSS effectively mitigates spectral collapse while maintaining a linear complexity. Extensive experiments on diverse benchmarks demonstrate that ELSS consistently delivers superior clustering accuracy over state-of-the-art methods.","doi":"10.24963/ijcai.2026/463","url":"https://ijcai.org/proceedings/2026/463","pdf":"https://ijcai.org/proceedings/2026/0463.pdf","markdown":"https://ijcai.org/proceedings/2026/463.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/463","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":"Machine Learning","track":"Main Track","number":463,"pages":{"first":4161,"last":4169},"keywords":["Machine Learning: Clustering","Machine Learning: Unsupervised learning","Machine Learning: Geometric learning","Machine Learning: Kernel methods"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}