Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5496324 | Physics Letters A | 2017 | 4 Pages |
Abstract
During the last decade, interaction data have accumulated exponentially in many fields and provide a new opportunity for cold start link prediction. It seems necessarily to take full advantages of diversified information. However, correlation between different interactions has to be pre-tested. Therefore, this paper abstracts complex systems as multi-relational networks, and employs latent space network model to extract low-dimensional factors of sub-networks and adopts likelihood ratio test to examine correlation between factors. Then, regression between target sub-networks and correlated auxiliary sub-networks could be established for cold start link prediction. Experiments on 8 bioinformatic data sets validate the effectiveness and potential of our strategy for network correlation analysis and cold-start link prediction.
Related Topics
Physical Sciences and Engineering
Physics and Astronomy
Physics and Astronomy (General)
Authors
Shun-yao Wu, Qi Zhang, Mei Wu,