Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6874302 | Journal of Computational Science | 2018 | 22 Pages |
Abstract
This paper studies link prediction, a recently emerged hot topic with many important applications, noticeably in complex network analysis. We propose a novel similarity-based approach which improves the well-known naive Bayes method by introducing a new tree augmented naive (TAN) Bayes probabilistic model. It makes better link predictions since the model alleviates the strong independency hypothesis among shared common neighbors to match the real-world situation. To obtain the latent correlation among common neighbors, we exploit mutual information to quantify the influence from neighbors' neighborhood. This yields a better performance than those methods which employing more local link/triangle structure information. In addition, the TAN model are easily adopted to other common neighbors-based methods such as AA and RA. Experimental results on synthetic and real-world networks show that our algorithms outperform the baseline methods, in terms of both effectiveness and efficiency.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Jiehua Wu,