{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:14:50Z","timestamp":1762341290584},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/469","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"3382-3388","source":"Crossref","is-referenced-by-count":27,"title":["Community Detection and Link Prediction via Cluster-driven Low-rank Matrix Completion"],"prefix":"10.24963","author":[{"given":"Junming","family":"Shao","sequence":"first","affiliation":[{"name":"Data Mining Lab, University of Electronic Science and Technology of China"}]},{"given":"Zhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Data Mining Lab, University of Electronic Science and Technology of China"}]},{"given":"Zhongjing","family":"Yu","sequence":"additional","affiliation":[{"name":"Data Mining Lab, University of Electronic Science and Technology of China"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"Data Mining Lab, University of Electronic Science and Technology of China"}]},{"given":"Yi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Data Mining Lab, University of Electronic Science and Technology of China"}]},{"given":"Qinli","family":"Yang","sequence":"additional","affiliation":[{"name":"Data Mining Lab, University of Electronic Science and Technology of China"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:49:32Z","timestamp":1564285772000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/469"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/469","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}