https://scholars.lib.ntu.edu.tw/handle/123456789/489741
標題: | Marine: Multi-relational network embeddings with relational proximity and node attributes | 作者: | Feng, M.-H. Hsu, C.-C. Li, C.-T. Yeh, M.-Y. SHOU-DE LIN |
公開日期: | 2019 | 起(迄)頁: | 470-479 | 來源出版物: | The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019 | 摘要: | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/489741 | DOI: | 10.1145/3308558.3313715 | SDG/關鍵字: | 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 |
顯示於: | 資訊工程學系 |
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