Link Prediction Using BenefitRanks in Weighted Networks

Link prediction in weighted network is an important task in Social Network Analysis. This problem aims at determining missing links in weighted networks. By taking advantage of the weights and structural information of networks, a mechanism for rating nodes´ authorities in terms of the value of weight, called Benefit Rank, is defined. This mechanism can flexibly collect different order neighbors´ information of nodes to complete the rating authority process for each node in weighted networks. Using Benefit Rank combined with the Weak Ties theory, similarity measures are proposed to estimate the emergence of future relationships between nodes in weighted networks. Extensive experiments were carried out on four real weighted networks. Compared with existing methods, our methods can provide higher accuracy for link prediction in weighted networks.