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Meta-learning adaptation network for few-shot link prediction in heterogeneous social networks

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成果类型:
期刊论文
作者:
Wang, Huan;Mi, Jiaxin;Guo, Xuan;Hu, Po
通讯作者:
Po Hu
作者机构:
PKU-Wuhan Institute for Artificial Intelligence, Wuhan, 100080, China
College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, 430079, China
School of Computer Science, Central China Normal University, Wuhan, 430079, China
[Guo, Xuan] The Computer Science and Engineering Department, University of North Texas, Denton, 76203, United States
通讯机构:
[Po Hu] H
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China<&wdkj&>School of Computer Science, Central China Normal University, Wuhan 430079, China
语种:
英文
关键词:
Heterogeneous social network;Link prediction;Meta-learning;Newly emerged link types
期刊:
Information Processing & Management
ISSN:
0306-4573
年:
2023
卷:
60
期:
5
页码:
103418
基金类别:
This work was supported by the National Social Science Fund of China under Grant No. 20BTQ068 .
机构署名:
本校为通讯机构
院系归属:
计算机学院
摘要:
Link prediction, which aims to predict future or missing links among nodes, is a crucial research problem in social network analysis. A unique few-shot challenge is link prediction on newly emerged link types without sufficient verification information in heterogeneous social networks, such as commodity recommendation on new categories. Most of current approaches for link prediction rely heavily on sufficient verified link samples, and almost ignore the shared knowledge between different link types. Hence, they tend to suffer from data scarcity...

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