Hidden link prediction based on node centrality and weak ties

作者：Liu, Haifeng^*; Hu, Zheng; Haddadi, Hamed; Tian, Hui

来源：EPL, 2013, 101(1): 18004.

DOI：10.1209/0295-5075/101/18004

• COMPLEX NETWORKS

Link prediction has been widely used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. In this context, similarity-based algorithms have become the mainstream. However, most of them take into account the contributions of each common neighbor equally to the connection likelihood of two nodes. This paper proposes a model for link prediction, which is based on the node centrality of common neighbors. Three node centralities are discussed: degree, closeness and betweenness centrality. In our model, each common neighbor plays a different role to the node connection likelihood according to their centralities. Moreover, the weak-tie theory is considered for improving the prediction accuracy. Finally, extensive experiments on five real-world networks show that the proposed model can outperform the Common Neighbor (CN) algorithm and gives competitively good prediction of or even better than Adamic-Adar (AA) index and Resource Allocation (RA) index.

• 出版日期2013-1
• 单位北京邮电大学