{"type":"ScholarlyArticle","title":"Community Detection and Link Prediction via Cluster-driven Low-rank Matrix Completion","authors":[{"name":"Junming Shao","slug":"junming-shao","given":"Junming","family":"Shao","affiliation":"Data Mining Lab , University of Electronic Science and Technology of China"},{"name":"Zhong Zhang","slug":"zhong-zhang","given":"Zhong","family":"Zhang","affiliation":"Data Mining Lab , University of Electronic Science and Technology of China"},{"name":"Zhongjing Yu","slug":"zhongjing-yu","given":"Zhongjing","family":"Yu","affiliation":"Data Mining Lab , University of Electronic Science and Technology of China"},{"name":"Jun Wang","slug":"jun-wang","given":"Jun","family":"Wang","affiliation":"Data Mining Lab , University of Electronic Science and Technology of China"},{"name":"Yi Zhao","slug":"yi-zhao","given":"Yi","family":"Zhao","affiliation":"Data Mining Lab , University of Electronic Science and Technology of China"},{"name":"Qinli Yang","slug":"qinli-yang","given":"Qinli","family":"Yang","affiliation":"Data Mining Lab , University of Electronic Science and Technology of China"}],"abstract":"Community detection and link prediction are highly dependent since knowing cluster structure as a priori will help identify missing links, and in return, clustering on networks with supplemented missing links will improve community detection performance. In this paper, we propose a Cluster-driven Low-rank Matrix Completion (CLMC), for performing community detection and link prediction simultaneously in a unified framework. To this end, CLMC decomposes the adjacent matrix of a target network as three additive matrices: clustering matrix, noise matrix and supplement matrix.  The community-structure and low-rank constraints are imposed on the clustering matrix, such that the noisy edges between communities are removed and the resulting matrix is an ideal block-diagonal matrix. Missing edges are further learned via low-rank matrix completion. Extensive experiments show that CLMC achieves state-of-the-art performance.","doi":"10.24963/ijcai.2019/469","url":"https://ijcai.org/proceedings/2019/469","pdf":"https://ijcai.org/proceedings/2019/0469.pdf","markdown":"https://ijcai.org/proceedings/2019/469.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/469","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":469,"pages":{"first":3382,"last":3388},"keywords":["Machine Learning: Data Mining","Machine Learning: Unsupervised Learning","Machine Learning Applications: Networks","Machine Learning: Clustering","Machine Learning: Learning Theory"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2010] Jian-Feng Cai, Emmanuel J Candès, and Zuowei Shen. 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