{"type":"ScholarlyArticle","title":"MASTER: across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation","authors":[{"name":"Sen Su","slug":"sen-su","given":"Sen","family":"Su","affiliation":"State Key Laboratory of Networking and Switching Technology , Beijing University of Posts ands Telecommunications , Beijing 100876 , China"},{"name":"Li Sun","slug":"li-sun","given":"Li","family":"Sun","affiliation":"State Key Laboratory of Networking and Switching Technology , Beijing University of Posts ands Telecommunications , Beijing 100876 , China"},{"name":"Zhongbao Zhang","slug":"zhongbao-zhang","given":"Zhongbao","family":"Zhang","affiliation":"State Key Laboratory of Networking and Switching Technology , Beijing University of Posts ands Telecommunications , Beijing 100876 , China"},{"name":"Gen Li","slug":"gen-li","given":"Gen","family":"Li","affiliation":"State Key Laboratory of Networking and Switching Technology , Beijing University of Posts ands Telecommunications , Beijing 100876 , China"},{"name":"Jielun Qu","slug":"jielun-qu","given":"Jielun","family":"Qu","affiliation":"State Key Laboratory of Networking and Switching Technology , Beijing University of Posts ands Telecommunications , Beijing 100876 , China"}],"abstract":"Recently, reconciling social networks receives significant attention. Most of the existing studies have\r\nlimitations in the following three aspects: multiplicity, comprehensiveness and robustness. To address\r\nthese three limitations, we rethink this problem and propose the MASTER framework, i.e., across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation. In this framework, we first design a novel Constrained Dual Embedding model by simultaneously embedding and reconciling multiple social networks to formulate our problem into a unified optimization. To address this optimization, we then design an effective algorithm called NS-Alternating. We also prove that this algorithm converges to KKT points. Through extensive experiments on real-world datasets, we demonstrate that MASTER outperforms the state-of-the-art approaches.","doi":"10.24963/ijcai.2018/537","url":"https://ijcai.org/proceedings/2018/537","pdf":"https://ijcai.org/proceedings/2018/0537.pdf","markdown":"https://ijcai.org/proceedings/2018/537.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/537","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":537,"pages":{"first":3863,"last":3869},"keywords":["Multidisciplinary Topics and Applications: Social Sciences","Multidisciplinary Topics and Applications: AI and the Web","Knowledge Representation and Reasoning: Information Fusion","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Cao et al., 2016] Shaosheng Cao, Wei Lu, and Qiongkai Xu. 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