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

arXiv: https://arxiv.org/abs/1902.08329 · HTML: https://mlsanity.com/paper/1902.08329/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2019.

## Built on by (most cited first, 24 of 24)

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- [HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction](https://mlsanity.com/paper/2304.07302/) (2023)
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- [Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations](https://mlsanity.com/paper/2308.07883/) (2023)
- [How to Bridge Spatial and Temporal Heterogeneity in Link Prediction? A Contrastive Method](https://mlsanity.com/paper/2411.00612/) (2024)
- [Trajectory Encoding Temporal Graph Networks](https://mlsanity.com/paper/2504.11386/) (2025)
- [MUST: Multi-Scale Structural-Temporal Link Prediction Model for UAV Ad Hoc Networks](https://mlsanity.com/paper/2505.09331/) (2025)
- [Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning](https://mlsanity.com/paper/2508.14859/) (2025)
- [Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks](https://mlsanity.com/paper/2605.26290/) (2026)

## Abstract (arXiv)

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.