We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
LNGM: A link prediction algorithm based on local neighbor gravity model.
- Authors
Xu, Yanjie; Ren, Tao; Sun, Shixiang
- 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.
- Subjects
GRAVITY model (Social sciences); ALGORITHMS; RECORD stores; FORECASTING; SOCIAL networks; RECOMMENDER systems
- Publication
International Journal of Modern Physics C: Computational Physics & Physical Computation, 2022, Vol 33, Issue 10, p1
- ISSN
0129-1831
- Publication type
Article
- DOI
10.1142/S0129183122501340