TY - GEN
T1 - An attention-based collaboration framework for multi-view network representation learning
AU - Meng, Qu
AU - Tang, Jian
AU - Shang, Jingbo
AU - Ren, Xiang
AU - Zhang, Ming
AU - Han, Jiawei
N1 - Funding Information:
Research was sponsored in part by the U.S. Army Research Lab. under Cooperative Agreement No. W911NF-09-2-0053 (NSCTA), National Science Foundation IIS-1320617 and IIS 16-18481, and grant 1U54GM114838 awarded by NIGMS through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative (www.bd2k.nih.gov). e views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the ocial policies of the U.S. Army Research Laboratory or the U.S. Government. e U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. Research was partially supported by the National Natural Science Foundation of China (NSFC Grant Nos. 61472006, 61772039 and 91646202).
Publisher Copyright:
© 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-theart approaches for network representation learning with a single view and other competitive approaches with multiple views.
AB - Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-theart approaches for network representation learning with a single view and other competitive approaches with multiple views.
UR - http://www.scopus.com/inward/record.url?scp=85037334134&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85037334134&partnerID=8YFLogxK
U2 - 10.1145/3132847.3133021
DO - 10.1145/3132847.3133021
M3 - Conference contribution
AN - SCOPUS:85037334134
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1767
EP - 1776
BT - CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 26th ACM International Conference on Information and Knowledge Management, CIKM 2017
Y2 - 6 November 2017 through 10 November 2017
ER -