典型文献
THS-GWNN:a deep learning framework for temporal network link prediction
文献摘要：
1 Introduction and main contributions
Link prediction for temporal networks aims to evaluate the likelihood of the future linkage among nodes,which has significant applications in social networks,biological net-works and traffic analysis[1],etc.Network embedding[2]is an important analytical tool for temporal network link predic-tion,which helps us better understand network evolution[3].
文献关键词：
中图分类号：
作者姓名：
Xian MO;Jun PANG;Zhiming LIU
作者机构：
College of Computer&Information Science,Southwest University,Chongqing 400715,China;Faculty of Science,Technology and Medicine&Interdisciplinary Centre for Security,Reliability and Trust,University of Luxembourg,Esch-sur-Alzette L-4364,Luxembourg
文献出处：
引用格式：
[1]Xian MO;Jun PANG;Zhiming LIU-.THS-GWNN:a deep learning framework for temporal network link prediction)[J].计算机科学前沿,2022(02):165-167
A类：
Link
B类：
THS,GWNN,deep,learning,framework,temporal,prediction,Introduction,main,contributions,networks,aims,evaluate,likelihood,future,linkage,among,nodes,which,has,significant,applications,social,biological,traffic,analysis,etc,Network,embedding,important,analytical,tool,helps,us,better,understand,evolution
AB值：
0.635839
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