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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yanjie Xu & Tao Ren & Shixiang Sun, 2022. "LNGM: A link prediction algorithm based on local neighbor gravity model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 33(10), pages 1-10, October.
    • Handle: RePEc:wsi:ijmpcx:v:33:y:2022:i:10:n:s0129183122501340
    DOI: 10.1142/S0129183122501340
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