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.
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