Open AccessLink prediction based on non-negative matrix factorization
Author(s) -
Bolun Chen,
Fenfen Li,
Senbo Chen,
Ronglin Hu,
Ling Chen
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0182968
Subject(s) - computer science , matrix decomposition , matrix (chemical analysis) , the internet , non negative matrix factorization , projection (relational algebra) , similarity (geometry) , data mining , link (geometry) , process (computing) , algorithm , sparse matrix , factorization , artificial intelligence , theoretical computer science , machine learning , computer network , eigenvalues and eigenvectors , materials science , physics , image (mathematics) , quantum mechanics , world wide web , composite material , gaussian , operating system
With the rapid expansion of internet, the complex networks has become high-dimensional, sparse and redundant. Besides, the problem of link prediction in such networks has also obatined increasingly attention from different types of domains like information science, anthropology, sociology and computer sciences. It makes requirements for effective link prediction techniques to extract the most essential and relevant information for online users in internet. Therefore, this paper attempts to put forward a link prediction algorithm based on non-negative matrix factorization. In the algorithm, we reconstruct the correlation between different types of matrix through the projection of high-dimensional vector space to a low-dimensional one, and then use the similarity between the column vectors of the weight matrix as the scoring matrix. The experiment results demonstrate that the algorithm not only reduces data storage space but also effectively makes the improvements of the prediction performance during the process of sustaining a low time complexity.