Temporal link prediction in directed networks based on self-attention mechanism

The development of graph neural networks (GCN) makes it possible to learn structural features from evolving complex networks. Even though a wide range of realistic networks are directed ones, few existing works investigated the properties of directed and temporal networks. In this paper, we address the problem of temporal link prediction in directed networks and propose a deep learning model based on GCN and self-attention mechanism, namely TSAM. The proposed model adopts an autoencoder architecture, which utilizes graph attentional layers to capture the structural feature of neighborhood nodes, as well as a set of graph convolutional layers to capture motif features. A graph recurrent unit layer with self-attention is utilized to learn temporal variations in the snapshot sequence. We run comparative experiments on four realistic networks to validate the effectiveness of TSAM. Experimental results show that TSAM outperforms most benchmarks under two evaluation metrics.

Automated summary

This paper investigates the properties of directed and temporal networks and proposes a deep learning model, TSAM, based on GCN and self-attention mechanism, to address the problem of temporal link prediction in directed networks. Experimental results show that TSAM outperforms most benchmarks under two evaluation metrics.