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SE-GRU: Structure Embedded Gated Recurrent Unit Neural Networks for Temporal Link Prediction

文献类型：期刊论文

作者Yin, Yanting3; Wu, Yajing2; Yang, Xuebing2; Zhang, Wensheng1,2; Yuan, Xiaojie3
刊名IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
出版日期2022-07-01
卷号9期号:4页码:2495-2509
关键词Time-frequency analysis Feature extraction Predictive models Optimization Topology Measurement Logic gates Temporal link prediction dynamic graphs graph embedding neural networks
ISSN号2327-4697
DOI10.1109/TNSE.2022.3164659
通讯作者Yang, Xuebing(yangxuebing2013@ia.ac.cn) ; Yuan, Xiaojie(yuanxj@nankai.edu.cn)
英文摘要Temporal link prediction on dynamic graphs is essential to various areas such as recommendation systems, social networks, and citation analysis, and thus attracts great attention in both research and industry fields. For complex graphs in real-world applications, although recent temporal link prediction methods perform well in predicting high-frequency and nearby connections, it becomes more challenging when considering low-frequency and earlier connections. In this work, we introduce a novel and elegant prediction architecture called Structure Embedded Gated Recurrent Unit (SE-GRU) neural networks, to strengthen the prediction robustness against frequency variation and occurrence delay of connections. The established SE-GRU embeds the structure for local topological characteristics to emphasize the different connection frequencies between nodes and captures the temporal dependencies to avoid losing valuable information caused by long-term changes. We realize neural network optimization considering three terms concerning reconstruction, structure, and evolution. The extensive experiments performed on three public datasets demonstrate the significant superiority of SE-GRU compared with 5 representative and state-of-the-art competitors under three evaluation metrics. The results validate the effectiveness and robustness of our proposed method, by showing that the frequencies and timestamps of connections have a little-to-no negative impact on prediction accuracy.
资助项目National Key R&D Program of China[2018AAA0102100] ; National Natural Science Foundation of China[U1936206] ; National Natural Science Foundation of China[61906190] ; National Natural Science Foundation of China[61906191] ; National Natural Science Foundation of China[62077031]
WOS研究方向Engineering ; Mathematics
语种英语
WOS记录号WOS:000818899600043
出版者IEEE COMPUTER SOC
资助机构National Key R&D Program of China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/49155]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Yang, Xuebing; Yuan, Xiaojie
作者单位1.Nankai Univ, Coll Comp Sci, Tianjin 300350, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
3.Nankai Univ, Coll Comp Sci, Tianjin Key Lab Network & Data Secur Technol, Tianjin 300350, Peoples R China
推荐引用方式
GB/T 7714		 Yin, Yanting,Wu, Yajing,Yang, Xuebing,et al. SE-GRU: Structure Embedded Gated Recurrent Unit Neural Networks for Temporal Link Prediction[J]. IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING,2022,9(4):2495-2509.
APA Yin, Yanting,Wu, Yajing,Yang, Xuebing,Zhang, Wensheng,&Yuan, Xiaojie.(2022).SE-GRU: Structure Embedded Gated Recurrent Unit Neural Networks for Temporal Link Prediction.IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING,9(4),2495-2509.
MLA Yin, Yanting,et al."SE-GRU: Structure Embedded Gated Recurrent Unit Neural Networks for Temporal Link Prediction".IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 9.4(2022):2495-2509.

入库方式： OAI收割

来源：自动化研究所

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