Dynamic link prediction by learning the representation of node-pair via graph neural networks

Many real-world networks are dynamic, whose structure keeps changing over time. Link prediction, which can foretell the emergence of future links, is one crucial task in dynamic network analysis. Compared to link prediction in static networks, it is more challenging and complicated in dynamic ones due to the dynamic nature. On the other hand, effective use of the information carried out by dynamic networks can enhance prediction accuracy. In this study, we presents a new end-to-end solution for dynamic link prediction, in which the representations of node-pairs can be effectively learned via an improved graph neural network and a nonlinear function by leveraging the structural information of individual snapshots, historical features from network evolution, and global knowledge of the collapsed network. The proposed method can effectively cope with the challenge of dynamic link prediction. Extensive tests are implemented on several dynamic networks to assess the prediction performance of our proposed method. The results on these networks demonstrate that our proposed method achieves superior effectiveness compared to the baselines in most cases.

Automated summary

In this study, a new end-to-end solution for dynamic link prediction is proposed, which effectively learns the representations of node-pairs by leveraging the structural information of individual snapshots, historical features from network evolution, and global knowledge of the collapsed network. Extensive tests on several dynamic networks demonstrate that the proposed method achieves superior effectiveness compared to the baselines in most cases.