Toward link prediction in directed social networks based on common interest and local community

Jinsong Li (), Jianhua Peng (), Shuxin Liu, Kai Wang () and Cong Li ()
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Jinsong Li: People’s Liberation Army Strategic Support Force, Information Engineering University, Zhengzhou, Henan 450001, P. R. China
Jianhua Peng: People’s Liberation Army Strategic Support Force, Information Engineering University, Zhengzhou, Henan 450001, P. R. China
Shuxin Liu: People’s Liberation Army Strategic Support Force, Information Engineering University, Zhengzhou, Henan 450001, P. R. China
Kai Wang: People’s Liberation Army Strategic Support Force, Information Engineering University, Zhengzhou, Henan 450001, P. R. China
Cong Li: People’s Liberation Army Strategic Support Force, Information Engineering University, Zhengzhou, Henan 450001, P. R. China

International Journal of Modern Physics C (IJMPC), 2020, vol. 31, issue 11, 1-18

Abstract: Link prediction has been widely applied in social network analysis. Existing studies on link prediction assume the network to be undirected, while most realistic social networks are directed. In this paper, we design a simple but effective method of link prediction in directed social networks based on common interest and local community. The proposed method quantifies the contributions of neighbors with analysis on the information exchange process among nodes. It captures both the essential motivation of link formation and the effect of local community in social networks. We validate the effectiveness of our method with comparative experiments on nine realistic networks. Empirical studies show that the proposed method is able to achieve better prediction performance under three standard evaluation metrics, with great robustness on the size of training set.

Keywords: Social network analysis; link prediction; directed social network; common interest; local community (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0129183120501600

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