Link prediction using semi-supervised learning
Year: 2012
Type: article
Abstract: Link prediction is the key issue in social network analysis,which aims to predict new links from the known networks.In real networks,there exist a large number of unlinked node pairs,whose intrinsic information can be helpful to the link prediction task.In this paper,link prediction is looked as a binary classification problem,where the semi-supervised learning techniques are used to take advantage of unlabeled data in learning process.Two semi-supervised paradigms are used:self-training and co-training.The experimental results in real datasets Enron and DBLP show that,exploiting unlabeled data can effectively improve the accuracy of the link prediction.
Author: Zheng-zhong ZHENG
Institution: Nanjing University of Posts and Telecommunications
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2354181961