Link prediction measures considering different neighbors’ effects and application in social networks

Peng Luo, Chong Wu () and Yongli Li ()
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Peng Luo: Harbin Institute of Technology, Harbin 150001, China
Chong Wu: Harbin Institute of Technology, Harbin 150001, China
Yongli Li: Northeastern University, Shenyang 110819, China

International Journal of Modern Physics C (IJMPC), 2017, vol. 28, issue 03, 1-17

Abstract: Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.

Keywords: Link prediction; social network; different neighbors’ effect; network analysis (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1142/S0129183117500334

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