Hidden link prediction based on node centrality and weak ties

Haifeng Liu, Zheng Hu, Hamed Haddadi, Hui Tian

Research output: Contribution to journalArticle

37 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number18004
JournalEPL
Volume101
Issue number1
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes

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predictions
resource allocation
interactions

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Hidden link prediction based on node centrality and weak ties. / Liu, Haifeng; Hu, Zheng; Haddadi, Hamed; Tian, Hui.

In: EPL, Vol. 101, No. 1, 18004, 01.01.2013.

Research output: Contribution to journalArticle

Liu, H, Hu, Z, Haddadi, H & Tian, H 2013, 'Hidden link prediction based on node centrality and weak ties', EPL, vol. 101, no. 1, 18004. https://doi.org/10.1209/0295-5075/101/18004
Liu, Haifeng ; Hu, Zheng ; Haddadi, Hamed ; Tian, Hui. / Hidden link prediction based on node centrality and weak ties. In: EPL. 2013 ; Vol. 101, No. 1.
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