Marine: Multi-relational network embeddings with relational proximity and node attributes

Ming Han Feng, Chin Chi Hsu, Cheng Te Li, Mi Yen Yeh, Shou De Lin

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

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.

原文English
主出版物標題The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
發行者Association for Computing Machinery, Inc
頁面470-479
頁數10
ISBN(電子)9781450366748
DOIs
出版狀態Published - 2019 五月 13
事件2019 World Wide Web Conference, WWW 2019 - San Francisco, United States
持續時間: 2019 五月 132019 五月 17

出版系列

名字The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019

Conference

Conference2019 World Wide Web Conference, WWW 2019
國家United States
城市San Francisco
期間19-05-1319-05-17

    指紋

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

引用此

Feng, M. H., Hsu, C. C., Li, C. T., Yeh, M. Y., & Lin, S. D. (2019). Marine: Multi-relational network embeddings with relational proximity and node attributes. 於 The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019 (頁 470-479). (The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3308558.3313715