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TDAN: Transferable Domain Adversarial Network for Link Prediction in Heterogeneous Social Networks

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成果类型:
期刊论文
作者:
Wang, Huan;Liu, Guoquan;Hu, Po
通讯作者:
Hu, P
作者机构:
[Wang, Huan] Huazhong Agr Univ, PKU Wuhan Inst Artificial Intelligence, Coll Informat, Wuhan 430070, Hubei, Peoples R China.
[Liu, Guoquan; Hu, Po; Hu, P] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
通讯机构:
[Hu, P ] C
Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
语种:
英文
关键词:
Transfer learning;link prediction;type-shared knowledge
期刊:
ACM Transactions on Knowledge Discovery from Data
ISSN:
1556-4681
年:
2024
卷:
18
期:
1
页码:
1–22
基金类别:
National Social Science Fund of China [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 has received increased attention in social network analysis. One of the unique challenges in heterogeneous social networks is link prediction in new link types without verified link information, such as recommending products to new overseas groups. Existing link prediction models tend to learn type-specific knowledge on specific link types and predict missing or future links on the same link types. However, because of the uncertainty of new link types in the evolving process of social networks, it is difficult to collect sufficient verified link information in new link types. T...

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