# GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction

arXiv: https://arxiv.org/abs/1812.04206 · HTML: https://mlsanity.com/paper/1812.04206/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2018.

## Built on by (most cited first, 30 of 47)

- [PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models](https://mlsanity.com/paper/2104.07788/) (2021)
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- [Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs](https://mlsanity.com/paper/2203.02150/) (2022)
- [Temporal Graph ODEs for Irregularly-Sampled Time Series](https://mlsanity.com/paper/2404.19508/) (2024)
- [On the Equivalence Between Temporal and Static Graph Representations for Observational Predictions](https://mlsanity.com/paper/2103.07016/) (2021)
- [BotDGT: Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers](https://mlsanity.com/paper/2404.15070/) (2024)
- [UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs](https://mlsanity.com/paper/2407.12269/) (2024)
- [Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling](https://mlsanity.com/paper/2505.18728/) (2025)
- [Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey](https://mlsanity.com/paper/2202.09363/) (2022)
- [Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching](https://mlsanity.com/paper/2402.04924/) (2024)
- [Representation Learning in Continuous-Time Dynamic Signed Networks](https://mlsanity.com/paper/2207.03408/) (2022)
- [Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction](https://mlsanity.com/paper/2211.08378/) (2022)
- [A Study of Joint Graph Inference and Forecasting](https://mlsanity.com/paper/2109.04979/) (2021)
- [Recurrent Temporal Revision Graph Networks](https://mlsanity.com/paper/2309.12694/) (2023)
- [Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction](https://mlsanity.com/paper/2110.03875/) (2021)
- [Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules](https://mlsanity.com/paper/2308.11034/) (2023)
- [DGNN-Booster: A Generic FPGA Accelerator Framework For Dynamic Graph Neural Network Inference](https://mlsanity.com/paper/2304.06831/) (2023)
- [Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs](https://mlsanity.com/paper/2402.16078/) (2024)
- [Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking](https://mlsanity.com/paper/2406.11920/) (2024)
- [Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories](https://mlsanity.com/paper/2406.17963/) (2024)
- [Are Large Language Models Good Temporal Graph Learners?](https://mlsanity.com/paper/2506.05393/) (2025)
- [Predicting Critical Nodes in Temporal Networks by Dynamic Graph Convolutional Networks](https://mlsanity.com/paper/2106.10419/) (2021)
- [Learning to Evolve on Dynamic Graphs](https://mlsanity.com/paper/2111.07032/) (2021)
- [Multi-representations Space Separation based Graph-level Anomaly-aware Detection](https://mlsanity.com/paper/2307.12994/) (2023)
- [MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs](https://mlsanity.com/paper/2406.10426/) (2024)
- [Hypergraph-Based Dynamic Graph Node Classification](https://mlsanity.com/paper/2412.20321/) (2024)
- [Understanding GNNs and Homophily in Dynamic Node Classification](https://mlsanity.com/paper/2504.20421/) (2025)
- [The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logics](https://mlsanity.com/paper/2505.11930/) (2025)

## Abstract (arXiv)

Dynamic link prediction is a research hot in complex networks area, especially for its wide applications in biology, social network, economy and industry. Compared with static link prediction, dynamic one is much more difficult since network structure evolves over time. Currently most researches focus on static link prediction which cannot achieve expected performance in dynamic network. Aiming at low AUC, high Error Rate, add/remove link prediction difficulty, we propose GC-LSTM, a Graph Convolution Network (GC) embedded Long Short Term Memory network (LTSM), for end-to-end dynamic link prediction. To the best of our knowledge, it is the first time that GCN embedded LSTM is put forward for link prediction of dynamic networks. GCN in this new deep model is capable of node structure learning of network snapshot for each time slide, while LSTM is responsible for temporal feature learning for network snapshot. Besides, current dynamic link prediction method can only handle removed links, GC-LSTM can predict both added or removed link at the same time. Extensive experiments are carried out to testify its performance in aspects of prediction accuracy, Error Rate, add/remove link prediction and key link prediction. The results prove that GC-LSTM outperforms current state-of-art method.