Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to compare network structures across populations. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT), which uses resampling-based permutation testing to compare network structures from two independent, cross-sectional data sets on invariance of (a) network structure, (b) edge (connection) strength, and (c) global strength. Performance of NCT is evaluated in simulations that show NCT to perform well in various circumstances for all three tests: The Type I error rate is close to the nominal significance level, and power proves sufficiently high if sample size and difference between networks are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed. Translational AbstractThe network approach, in which psychological constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure, to a more comparative stance, in which the goal is to compare network structures across groups. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT). NCT is a statistical test that compares two network structures on three types of characteristics. Performance of NCT is evaluated by means of a simulation study. Simulated data shows that NCT performs well in various circumstances for all three tests: when the groups are simulated to be similar, the error rate (i.e., NCT indicating that they are different, while the simulated networks are similar) is adequately low, and when the groups are simulated to be different, the ability to detect a difference is sufficiently high when the difference between simulated networks and the sample size are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed.