{"type":"ScholarlyArticle","title":"TOTF: Missing-Aware Encoders for Clustering on Multi-View Incomplete Attributed Graphs","authors":[{"name":"Mengyao Li","slug":"mengyao-li","given":"Mengyao","family":"Li","affiliation":""},{"name":"Xu Zhou","slug":"xu-zhou","given":"Xu","family":"Zhou","affiliation":""},{"name":"Jiapeng Zhang","slug":"jiapeng-zhang","given":"Jiapeng","family":"Zhang","affiliation":""},{"name":"Zhibang Yang","slug":"zhibang-yang","given":"Zhibang","family":"Yang","affiliation":""},{"name":"Cen Chen","slug":"cen-chen","given":"Cen","family":"Chen","affiliation":""},{"name":"Kenli Li","slug":"kenli-li","given":"Kenli","family":"Li","affiliation":""}],"abstract":"As the network data in real life become multi-modal and multi-relational, multi-view attributed graphs have garnered significant attention. Numerous methods have achieved excellent performance in multi-view attributed graph clustering; however, they cannot efficiently handle incomplete attribute scenarios, which are prevalent in many real-life applications. Inspired by this, we investigate the problem of multi-view incomplete attributed graph clustering for the first time. In particular, the TOTF (Train Once Then Freeze) framework is designed to train missing-aware encoders that capture view-specific information while ignoring the impact of incomplete attributes, and then employs frozen encoders to uncover common information driven by clustering. After that, we propose a correlation strength-aware graph neural network on the basis of the inherent relationships among attributes to enhance accuracy. It is proven theoretically that traditional Generative Adversarial Networks (GANs) are unable to generate the unique real distribution. To address this issue, we further introduce the missing-position reminder mechanism into our intra-view adversarial games for better clustering results. Extensive experimental results demonstrate that our method achieves up to a 17% improvement in accuracy over the state-of-the-art methods. The source code is available at https://anonymous.4open.science/r/TOTF-main.","doi":"10.24963/ijcai.2025/340","url":"https://ijcai.org/proceedings/2025/340","pdf":"https://ijcai.org/proceedings/2025/0340.pdf","markdown":"https://ijcai.org/proceedings/2025/340.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/340","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":"Data Mining","track":"Main Track","number":340,"pages":{"first":3054,"last":3062},"keywords":["Machine Learning: ML: Clustering","Data Mining: DM: Mining graphs"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}