{"type":"ScholarlyArticle","title":"Scalable Multiplex Network Embedding","authors":[{"name":"Hongming Zhang","slug":"hongming-zhang","given":"Hongming","family":"Zhang","affiliation":"Department of CSE , The Hong Kong University of Science and Technology"},{"name":"Liwei Qiu","slug":"liwei-qiu","given":"Liwei","family":"Qiu","affiliation":"Tencent Technology (SZ) Co. , Ltd. , China"},{"name":"Lingling Yi","slug":"lingling-yi","given":"Lingling","family":"Yi","affiliation":"Tencent Technology (SZ) Co. , Ltd. , China"},{"name":"Yangqiu Song","slug":"yangqiu-song","given":"Yangqiu","family":"Song","affiliation":"Department of CSE , The Hong Kong University of Science and Technology"}],"abstract":"Network embedding has been proven to be helpful for many real-world problems. In this paper, we present a scalable multiplex network embedding model to represent information of multi-type relations into a unified embedding space. To combine information of different types of relations while maintaining their distinctive properties, for each node, we propose one high-dimensional common embedding and a lower-dimensional additional embedding for each type of relation. Then multiple relations can be learned jointly based on a unified network embedding model. We conduct experiments on two tasks: link prediction and node classification using six different multiplex networks. On both tasks, our model achieved better or comparable performance compared to current state-of-the-art models with less memory use.","doi":"10.24963/ijcai.2018/428","url":"https://ijcai.org/proceedings/2018/428","pdf":"https://ijcai.org/proceedings/2018/0428.pdf","markdown":"https://ijcai.org/proceedings/2018/428.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/428","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":"Machine Learning","track":"Main track","number":428,"pages":{"first":3082,"last":3088},"keywords":["Multidisciplinary Topics and Applications: Social Sciences","Machine Learning: Dimensionality Reduction and Manifold Learning","Machine Learning Applications: Big data ; Scalability","Machine Learning Applications: Networks","Machine Learning: Unsupervised Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Adamic and Adar, 2003] Lada A. 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