  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 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 paper, 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 dynamic
network link prediction methods and obtain the state-of-the-art results.
