The existing link prediction methods for directed networks only consider single-type network structures but ignore some key network structures, which leads to the decrease of prediction accuracy. To solve this problem, this paper proposed a link prediction framework which combined multi-type directed network structure and non-negative matrix factorization to preserve local and global structure information. Firstly, it mapped the adjacency matrix of directed network to the low-dimensional latent space to preserve the directional link of the original network. Secondly, it fused four key directed structural similarities including DCN, DAA, DRA and potential theory(BF) by 2-norm and normalised Laplacian to maintain information on the structure of multi-type networks. Then, it proposed four link prediction models NMF-DNS-DCN, NMF-DNS-DAA, NMF-DNS-DRA and NMF-DNS-BF respectively. Finally, this paper enabled multiplicative update rules to learn the parameters of the four models and proved the convergence of the proposed algorithms. Compared with the existing representative methods on 8 real-world directed networks, the AUC, recall and F₁ of the proposed model is increased by 5.3%, 7.8% and 6%, respectively.

[2022-02-18] Accepted Paper /doc/2021.12.0656/v1

[2022-07-05] Printed Article /doc/2021.12.0656/v2