Weighted synthetical influence of degree and H-index in link prediction of complex networks

Abstract

Link prediction based on traditional models have attracted many interests recently. Among all models, the ones based on topological similarity have achieved great success. However, researchers pay more attention to links, but less to endpoint influence. After profound investigation, we find that the synthesis of degree and H-index plays an important role in modeling endpoint influence. So, in this paper, we propose link prediction models based on weighted synthetical influence, exploring the role of H-index and degree in endpoint influence measurement. Experiments on 12 real-world networks show that the proposed models can provide higher accuracy.