https://scholars.lib.ntu.edu.tw/handle/123456789/489741
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Feng, M.-H. | en_US |
dc.contributor.author | Hsu, C.-C. | en_US |
dc.contributor.author | Li, C.-T. | en_US |
dc.contributor.author | Yeh, M.-Y. | en_US |
dc.contributor.author | SHOU-DE LIN | en_US |
dc.creator | Feng, M.-H.;Hsu, C.-C.;Li, C.-T.;Yeh, M.-Y.;Lin, S.-D. | - |
dc.date.accessioned | 2020-05-04T08:04:50Z | - |
dc.date.available | 2020-05-04T08:04:50Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/489741 | - |
dc.description.abstract | Network 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. | - |
dc.relation.ispartof | The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019 | - |
dc.subject.classification | [SDGs]SDG14 | - |
dc.subject.other | Classification (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; Embeddings | - |
dc.title | Marine: Multi-relational network embeddings with relational proximity and node attributes | en_US |
dc.type | conference paper | en |
dc.identifier.doi | 10.1145/3308558.3313715 | - |
dc.identifier.url | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066903914&doi=10.1145%2f3308558.3313715&partnerID=40&md5=f6b9ebbe2ddcf1e4ac5870834a93d714 | - |
dc.relation.pages | 470-479 | - |
item.openairetype | conference paper | - |
item.fulltext | no fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | Networking and Multimedia | - |
crisitem.author.dept | Computer Science and Information Engineering | - |
crisitem.author.dept | FinTech Center | - |
crisitem.author.dept | Center for Artificial Intelligence and Advanced Robotics | - |
crisitem.author.orcid | 0000-0001-9970-1250 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
Appears in Collections: | 資訊工程學系 |
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