Path extension similarity link prediction method based on matrix algebra in directed networks

Traditional link prediction methods are generally only calculated for the neighbor information of nodes, and the network path between nodes has not been fully utilized. Therefore, this paper proposes a directed network link prediction method based on path extension similarity to improve the prediction accuracy of potential edges of network nodes. Firstly, the mathematical definition of each local index is expressed in matrix form through matrix algebra; secondly, according to the algorithm principle of global and quasi-local indices, the extension form of local indices is clarified; and the path extension of each local index is carried out respectively; finally, multiple real data sets are used to analyze the benchmark indices and extended indices. The results of the AUC and Precision evaluation metrics show that the path extension similarity proposed in this paper has higher accuracy and stronger robustness than the benchmark indices.