The statistical inference of network data has become a hot topic in statistical research in recent years. The independence assumption among sample data in traditional models often fails to meet the analytical demands of modern network-linked data. This work studies the independent effect of each network node in the network-linked data, and based on the idea of fusion penalty, the independent effect of the associated nodes is converged. Knockoff variables construct covariates independent of the target variable by imitating the structure of the original variable. With the help of Knockoff variables, this study proposes a general method framework for variable selection for network-linked data (NLKF). The study proves that NLKF can control the false discovery rate (FDR) at the target level and has higher statistical power than the Lasso variable selection method. When the covariance of the original data is unknown, the covariance matrix using the estimation still has good statistical properties. Finally, combining the 200 factor samples of more than 4 000 stocks in the A-share market and their network relations constructed by Shenyin Wanguo’s first-level industry classification, an example of the application in the field of financial engineering is given.