Abstract: In order to improve the performance of complex network link prediction,topology similarity and XGBoost algorithm are used to complete link prediction in complex network.According to the topological structure of complex network,the adjacency matrix is established to solve the common neighbor set.Then the similarity score function of complex network is calculated according to the topological similarity theory.The score function and weight parameters of each time window are taken as input,and XGBoost algorithm is used to realize the link prediction of complex network.By setting two regularization coefficients of XGBoost algorithm through differentiation,the influence on link prediction accuracy is tested,and the optimal regularization coefficient is obtained,thus a stable XGBoost link prediction model is obtained.The experimental results show that,compared with the common network link prediction algorithms,the prediction accuracy based on topology similarity and XGBoost algorithm has obvious advantages,and the prediction time performance is smaller than other algorithms,especially suitable for large-scale complex network link prediction.

Key words: Complex network, Link prediction, Regularization, Time window, Topology similarity, XGBoost algorithm