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 13 → 2019 五月 17 |
出版系列
| 名字 | The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019 |
|---|
Conference
| Conference | 2019 World Wide Web Conference, WWW 2019 |
|---|---|
| 國家 | United States |
| 城市 | San Francisco |
| 期間 | 19-05-13 → 19-05-17 |
指紋
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Software
引用此文
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Marine : Multi-relational network embeddings with relational proximity and node attributes. / Feng, Ming Han; Hsu, Chin Chi; Li, Cheng-Te; Yeh, Mi Yen; Lin, Shou De.
The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. Association for Computing Machinery, Inc, 2019. p. 470-479 (The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019).研究成果: Conference contribution
TY - GEN
T1 - Marine
T2 - Multi-relational network embeddings with relational proximity and node attributes
AU - Feng, Ming Han
AU - Hsu, Chin Chi
AU - Li, Cheng-Te
AU - Yeh, Mi Yen
AU - Lin, Shou De
PY - 2019/5/13
Y1 - 2019/5/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85066903914&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066903914&partnerID=8YFLogxK
U2 - 10.1145/3308558.3313715
DO - 10.1145/3308558.3313715
M3 - Conference contribution
AN - SCOPUS:85066903914
T3 - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
SP - 470
EP - 479
BT - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
PB - Association for Computing Machinery, Inc
ER -