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

TL;DR

GC-LSTM is a novel deep learning model combining Graph Convolution Networks and LSTM to improve dynamic link prediction accuracy, capable of predicting both added and removed links in evolving networks.

Contribution

It introduces the first GCN-embedded LSTM model for dynamic networks, enhancing prediction of both added and removed links with superior accuracy.

Findings

Outperforms existing methods in accuracy and error rate

Capable of predicting both added and removed links

Effective in key link prediction tasks

Abstract

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.