Graph regularized nonnegative matrix factorization for link prediction in directed temporal networks using PageRank centrality

Temporal link prediction aims to predict new links at time T + 1 based on a given temporal network from time 1 to T. Some existing temporal link prediction methods only consider the naturally links direction formation but ignore the local and global information of temporal networks. To address this issue, in this paper, we propose a novel graph regularized nonnegative matrix factorization algorithm for the problem of temporal link prediction by using PageRank centrality and asymmetric link clustering coefficient, referred to as the GNMFCA. Specifically, we use graph regularization to capture the local information of each slice in temporal networks and PageRank centrality to compute the importance of nodes in each slice, which captures the global information of each slice in temporal networks. By jointly optimizing them in a nonnegative matrix factorization model, the proposed model can preserve both the local and global information simultaneously. Besides, we propose effective multiplicative updating rules to solve the proposed model and provide the convergence analysis of the iterative algorithm. Finally, we perform numerical experiments on eight artificial and four real-world temporal networks to demonstrate that GNMFCA outperforms some existing temporal link prediction algorithms. (c) 2022 Elsevier Ltd.

Automated summary

This paper proposes a novel graph regularized nonnegative matrix factorization algorithm for temporal link prediction, which considers both the local and global information of the temporal network. Experimental results show that the proposed algorithm outperforms existing methods.