Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks

2018 ◽  
Vol 496 ◽  
pp. 121-136 ◽  
Author(s):  
Xiaoke Ma ◽  
Penggang Sun ◽  
Yu Wang
Keyword(s):  
Matrix Factorization ◽  
Link Prediction ◽  
Nonnegative Matrix Factorization ◽  
Dynamic Networks ◽  
Nonnegative Matrix

scholarly journals Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale Blocks

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Enming Dong ◽  
Jianping Li ◽  
Zheng Xie
Keyword(s):  
Matrix Factorization ◽  
Link Prediction ◽  
Latent Variable ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Prediction Method ◽  
Original Method ◽  
Divide And Conquer ◽  
Low Rank ◽  
Variable Model

Low rank matrices approximations have been used in link prediction for networks, which are usually global optimal methods and lack of using the local information. The block structure is a significant local feature of matrices: entities in the same block have similar values, which implies that links are more likely to be found within dense blocks. We use this insight to give a probabilistic latent variable model for finding missing links by convex nonnegative matrix factorization with block detection. The experiments show that this method gives better prediction accuracy than original method alone. Different from the original low rank matrices approximations methods for link prediction, the sparseness of solutions is in accord with the sparse property for most real complex networks. Scaling to massive size network, we use the block information mapping matrices onto distributed architectures and give a divide-and-conquer prediction method. The experiments show that it gives better results than common neighbors method when the networks have a large number of missing links.

Sign in / Sign up

Export Citation Format

Share Document