This study introduces module preservation statistics to assess whether the properties of network modules are preserved across different networks. The authors distinguish between cross-tabulation-based and network-based preservation statistics. Cross-tabulation-based methods require module assignments in both the reference and test networks, while network-based methods use adjacency matrices or numeric data to evaluate preservation without requiring module assignments in the test network. The study shows that correlation networks facilitate the definition of powerful module preservation statistics. It also highlights that module preservation is different from cluster preservation and that aggregating multiple preservation statistics into summary statistics can provide a more comprehensive assessment. The authors apply these methods to six gene co-expression network applications, including the preservation of cholesterol biosynthesis modules in mouse tissues, comparison of human and chimpanzee brain networks, and preservation of selected KEGG pathways between human and chimpanzee brains. They find that while some modules are preserved across species, others are not, with apoptosis genes showing differential expression between humans and chimpanzees. The study also demonstrates that module preservation statistics can detect aspects of preservation missed by traditional cluster validation methods. The results show that modules expressed in evolutionarily conserved brain areas are more strongly preserved than those in regions that have undergone significant evolutionary changes. The authors conclude that module preservation statistics are useful for studying differences in the modular structure of networks and provide a framework for evaluating the reproducibility of network modules.This study introduces module preservation statistics to assess whether the properties of network modules are preserved across different networks. The authors distinguish between cross-tabulation-based and network-based preservation statistics. Cross-tabulation-based methods require module assignments in both the reference and test networks, while network-based methods use adjacency matrices or numeric data to evaluate preservation without requiring module assignments in the test network. The study shows that correlation networks facilitate the definition of powerful module preservation statistics. It also highlights that module preservation is different from cluster preservation and that aggregating multiple preservation statistics into summary statistics can provide a more comprehensive assessment. The authors apply these methods to six gene co-expression network applications, including the preservation of cholesterol biosynthesis modules in mouse tissues, comparison of human and chimpanzee brain networks, and preservation of selected KEGG pathways between human and chimpanzee brains. They find that while some modules are preserved across species, others are not, with apoptosis genes showing differential expression between humans and chimpanzees. The study also demonstrates that module preservation statistics can detect aspects of preservation missed by traditional cluster validation methods. The results show that modules expressed in evolutionarily conserved brain areas are more strongly preserved than those in regions that have undergone significant evolutionary changes. The authors conclude that module preservation statistics are useful for studying differences in the modular structure of networks and provide a framework for evaluating the reproducibility of network modules.