A general link prediction method based on path node information and source node information

Abstract

Link prediction in complex networks involves forecasting unknown or future connections. Traditional methods often rely heavily on network topology information. However, in complex networks with significant attribute information (i.e., attributed networks), relying solely on topology information often leads to limited accuracy in predicting node connections. To address this issue, this study explores link prediction methods for weighted/unweighted and attributed/non-attributed networks. A novel node similarity is introduced, which comprehensively considers multiple factors. Based on structural information, attribute information, and weight information, a general link prediction framework is proposed for four different network types. This framework contains three core modules: a structural similarity module, an attribute similarity module, and a weighted similarity module. Using these modules, four global similarity measurements are defined for different network types. Taking weighted and attributed networks as an example, a link prediction algorithm is designed, and three key parameters are analyzed. To validate the effectiveness of the proposed algorithms, experiments are conducted on four types of real-world network datasets. The experimental results demonstrate that the proposed algorithms exhibit significant advantages in terms of prediction accuracy and robustness.