标题:Rank2vec: Learning node embeddings with local structure and global ranking
作者:Zhou, Hui; Zhao, Zhongying; Li, Chao; Liang, Yongquan; Zeng, Qingtian
作者机构:[Zhou, Hui; Zhao, Zhongying; Li, Chao; Liang, Yongquan; Zeng, Qingtian] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao, Shandong, Peoples 更多
通讯作者:Zhao, Zhongying;Zhao, ZY
通讯作者地址:[Zhao, ZY]Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao, Shandong, Peoples R China.
来源:EXPERT SYSTEMS WITH APPLICATIONS
出版年:2019
卷:136
页码:276-287
DOI:10.1016/j.eswa.2019.06.045
关键词:Network representation; Node embedding; Local structure; Global role
摘要:Network embedding aims to project each node to a low-dimensional representation while maximally preserving network structure and inherent properties. It is attracting tremendous attention due to its great significance in various network analysis tasks, such as expert finding, relationship prediction, people classification, community identification, etc. However, the existing embedding methods mainly focus on capturing the microscopic structure of the nodes in the network. But they ignore the different global roles played by the nodes, resulting in the limitations in the mesoscopic and macroscopic tasks. To address this problem, we propose a novel network embedding method named Rank2vec. It considers both local structure and global structural roles. Thus it enables the learned representations to preserve both the microscopic and macroscopic information. To evaluate the proposed model, we conduct some extensive experiments on the task of multi-label classification on several real data sets. The experimental results have shown that the Rank2vec achieves significant improvement than state-of-the-art methods. (C) 2019 Elsevier Ltd. All rights reserved.
收录类别:EI;SCOPUS;SCIE
WOS核心被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067837665&doi=10.1016%2fj.eswa.2019.06.045&partnerID=40&md5=d64c9c142a452b1cc2e964ac65377cde
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