{"type":"ScholarlyArticle","title":"Graph Embedded Contrastive Learning for Multi-View Clustering","authors":[{"name":"Hongqing He","slug":"hongqing-he","given":"Hongqing","family":"He","affiliation":""},{"name":"Jie Xu","slug":"jie-xu","given":"Jie","family":"Xu","affiliation":""},{"name":"Guoqiu Wen","slug":"guoqiu-wen","given":"Guoqiu","family":"Wen","affiliation":""},{"name":"Yazhou Ren","slug":"yazhou-ren","given":"Yazhou","family":"Ren","affiliation":""},{"name":"Na Zhao","slug":"na-zhao","given":"Na","family":"Zhao","affiliation":""},{"name":"Xiaofeng Zhu","slug":"xiaofeng-zhu","given":"Xiaofeng","family":"Zhu","affiliation":""}],"abstract":"Recently, numerous multi-view clustering (MVC) and multi-view graph clustering (MVGC) methods have been proposed. Despite significant progress, they still face two issues: I) MVC and MVGC are often developed independently for multi-view and multi-graph data. They have redundancy but lack a unified methodology to combine their strengths. II) Contrastive learning is usually adopted to explore the associations across multiple views. However, traditional contrastive losses ignore the neighbor relationship in multi-view scenarios and easily lead to false associations in sample pairs. To address these issues, we propose Graph Embedded Contrastive Learning for Multi-View Clustering. Concretely, we propose a process of view-specific pre-training with adaptive graph convolution to make our method compatible with both multi-view and multi-graph data, which aggregates the graph information into data and leverages autoencoders to learn view-specific representations. Furthermore, to explore the view-cross associations, we introduce the process of view-cross contrastive learning and clustering, where we propose the graph-guided contrastive learning that can generate global graph to mitigate the false association issue as well as the cluster-guided contrastive clustering for improving the model robustness. Finally, extensive experiments demonstrate that our method achieves superior performance on both MVC and MVGC tasks.","doi":"10.24963/ijcai.2025/594","url":"https://ijcai.org/proceedings/2025/594","pdf":"https://ijcai.org/proceedings/2025/0594.pdf","markdown":"https://ijcai.org/proceedings/2025/594.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/594","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":594,"pages":{"first":5336,"last":5344},"keywords":["Machine Learning: ML: Unsupervised learning","Machine Learning: ML: Multi-view learning","Machine Learning: ML: Self-supervised Learning","Machine Learning: ML: Clustering"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}