LNGM: A link prediction algorithm based on local neighbor gravity model

Yanjie Xu (), Tao Ren and Shixiang Sun ()
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Yanjie Xu: Software College, Northeastern University, No. 195, Chuangxin Road, Hunnan District, Shenyang 110169, P. R. China
Tao Ren: Software College, Northeastern University, No. 195, Chuangxin Road, Hunnan District, Shenyang 110169, P. R. China
Shixiang Sun: Software College, Northeastern University, No. 195, Chuangxin Road, Hunnan District, Shenyang 110169, P. R. China

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 10, 1-10

Abstract: Link prediction is a fundamental study with a variety of applications in complex network, which has attracted increased attention. Link prediction often can be used to recommend new friends in social networks, as well as recommend new products based on earlier shopping records in recommender systems, which brings considerable benefits for companies. In this work, we propose a new link prediction algorithm Local Neighbor Gravity Model (LNGM) algorithm, which is based on gravity and neighbors (1-hop and 2-hop), to suggest the formation of new links in complex networks. Extensive experiments on nine real-world datasets validate the superiority of LNGM on eight different benchmark algorithms. The results further validate the improved performance of LNGM.

Keywords: Complex network; neighbor; gravity model; link prediction (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0129183122501340

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