Journal
EPL
Volume 101, Issue 1, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1209/0295-5075/101/18004
Keywords
-
Categories
Funding
- Funds for Creative Research Groups of NSFC [61121001]
- Program for Changjiang Scholars and Innovative Research Team in MoE [IRT1049]
- [2011ZX03005-004-02]
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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. Copyright (C) EPLA, 2013
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