{"type":"ScholarlyArticle","title":"Joint Link Prediction and Network Alignment via Cross-graph Embedding","authors":[{"name":"Xingbo Du","slug":"xingbo-du","given":"Xingbo","family":"Du","affiliation":"School of Computer Science and Software Engineering , East China Normal University; KLATASDS-MOE , East China Normal University"},{"name":"Junchi Yan","slug":"junchi-yan","given":"Junchi","family":"Yan","affiliation":"Department of Computer Science and Engineering & MoE Key Lab of Artificial Intelligence , Shanghai Jiao Tong University; KLATASDS-MOE , East China Normal University"},{"name":"Hongyuan Zha","slug":"hongyuan-zha","given":"Hongyuan","family":"Zha","affiliation":"School of Computer Science and Software Engineering , East China Normal University; KLATASDS-MOE , East China Normal University"}],"abstract":"Link prediction and network alignment are two important problems in social network analysis and other network related applications. Considerable efforts have been devoted to these two problems while often in an independent way to each other. In this paper we argue that these two tasks are relevant and present a joint link prediction and network alignment framework, whereby a novel cross-graph node embedding technique is devised to allow for information propagation. Our approach can either work with a few initial vertex correspondence as seeds, or from scratch. By extensive experiments on public benchmark, we show that link prediction and network alignment can benefit to each other especially for improving the recall for both tasks.","doi":"10.24963/ijcai.2019/312","url":"https://ijcai.org/proceedings/2019/312","pdf":"https://ijcai.org/proceedings/2019/0312.pdf","markdown":"https://ijcai.org/proceedings/2019/312.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/312","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 A-L","track":"Main track","number":312,"pages":{"first":2251,"last":2257},"keywords":["Machine Learning: Semi-Supervised Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Al Hasan et al., 2006] Mohammad Al Hasan, Vineet Chaoji, Saeed Salem, and Mohammed Zaki. 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