E-LSTM-D : A Deep Learning Framework for Dynamic Network Link Prediction

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Jinyin Chen
  • Jian Zhang
  • Xuanheng Xu
  • Chenbo Fu
  • Dan Zhang
  • Qi Xuan

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number8809903
Pages (from-to)3699-3712
Journal / PublicationIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume51
Issue number6
Online published22 Aug 2019
Publication statusPublished - Jun 2021

Abstract

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.

Research Area(s)

  • Dynamic network, encoder-decoder, link prediction, long short-term memory (LSTM), network embedding

Citation Format(s)

E-LSTM-D : A Deep Learning Framework for Dynamic Network Link Prediction. / Chen, Jinyin; Zhang, Jian; Xu, Xuanheng et al.

In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 51, No. 6, 8809903, 06.2021, p. 3699-3712.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review