Feng, M.-H.M.-H.FengHsu, C.-C.C.-C.HsuLi, C.-T.C.-T.LiYeh, M.-Y.M.-Y.YehSHOU-DE LIN2020-05-042020-05-042019https://scholars.lib.ntu.edu.tw/handle/123456789/489741Network embedding aims at learning an effective vector transformation for entities in a network. We observe that there are two diverse branches of network embedding: for homogeneous graphs and for multi-relational graphs. This paper then proposes MARINE, a unified embedding framework for both homogeneous and multi-relational networks to preserve both the proximity and relation information. We also extend the framework to incorporate existing features of nodes in a graph, which can further be exploited for the ensemble of embedding. Our solution possesses complexity linear to the number of edges, which is suitable for large-scale network applications. Experiments conducted on several real-world network datasets, along with applications in link prediction and multi-label classification, exhibit the superiority of our proposed MARINE. © 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.[SDGs]SDG14Classification (of information); Marine applications; World Wide Web; Homogeneous network; Knowledge graphs; Large-scale network; Multi label classification; Multi-relational networks; Real-world networks; Relation information; Vector transformation; Embeddingsconference paper10.1145/3308558.3313715https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066903914&doi=10.1145%2f3308558.3313715&partnerID=40&md5=f6b9ebbe2ddcf1e4ac5870834a93d714