LAUSR

Link prediction of complex network intends to estimate the probability of existence of links between two nodes. In order to improve link prediction accuracy and fully exploit the potentialities of nodes, many studies focus more on the influence of degree on nodes but less on the hybrid influence of degree and H-index. The nodes with a larger degree have more neighbors, and the nodes with larger H-index have more neighbors of neighbors. Meanwhile, weak ties consisting of neighbors with a small degree have powerful strength of intermediary ability and a high probability of passing similarity. A novel link prediction model is proposed considering the hybrid influence of degree and H-index and weak ties, which is called Hybrid Weak Influence, marked as HWI. After experimenting with nine real datasets, the results show that this method can significantly improve the link prediction accuracy, compared with the empirical methods: Common Neighbors (CN), Resource-Allocation (RA) and Adamic/Adar (AA). Meanwhile, the computation complexity is less than the long path algorithm of LP, SRW, PCEN.