计算机科学
链接(几何体)
人工智能
编码器
深度学习
网络体系结构
循环神经网络
期限(时间)
机器学习
动态网络分析
人工神经网络
计算机网络
计算机安全
量子力学
操作系统
物理
作者
Jinyin Chen,Jian Zhang,Xuanheng Xu,Chenbo Fu,Dan Zhang,Qingpeng Zhang,Qi Xuan
出处
期刊:IEEE transactions on systems, man, and cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2021-06-01
卷期号:51 (6): 3699-3712
被引量:108
标识
DOI:10.1109/tsmc.2019.2932913
摘要
Predicting the potential relations between nodes in networks, known as link prediction, has long been a challenge in network science. However, most studies just focused on link prediction of static network, while real-world networks always evolve over time with the occurrence and vanishing of nodes and links. Dynamic network link prediction (DNLP) thus has been attracting more and more attention since it can better capture the evolution nature of networks, but still most algorithms fail to achieve satisfied prediction accuracy. Motivated by the excellent performance of long short-term memory (LSTM) in processing time series, in this article, we propose a novel encoder-LSTM-decoder (E-LSTM-D) deep learning model to predict dynamic links end to end. It could handle long-term prediction problems, and suits the networks of different scales with fine-tuned structure. To the best of our knowledge, it is the first time that LSTM, together with an encoder-decoder architecture, is applied to link prediction in dynamic networks. This new model is able to automatically learn structural and temporal features in a unified framework, which can predict the links that never appear in the network before. The extensive experiments show that our E-LSTM-D model significantly outperforms newly proposed DNLP methods and obtain the state-of-the-art results.
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