Strengthening Social Networks Analysis by Networks Fusion

Abstract

The relationship extraction and fusion of networks are the hotspots of current research in social network mining. Most previous work is based on single-source data. However, the relationships portrayed by single-source data are not sufficient to characterize the relationships of the real world. To solve this problem, a Semi-supervised Fusion framework for Multiple Network (SFMN), using gradient boosting decision tree algorithm (GBDT) to fuse the information of multi-source networks into a single network, is proposed in this paper. Our framework aims to take advantage of multi-source networks fusion to enhance the accuracy of the network construction. The experiment shows that our method optimizes the structural and community accuracy of social networks which makes our framework outperforms several state-of-the-art methods.