The paper discusses the resolution limit in community detection using modularity optimization. Modularity is a measure used to evaluate the quality of a network partition into communities. The authors show that modularity optimization may fail to identify small modules, even if they are clearly defined, due to an intrinsic scale dependent on the total number of links (L) and the interconnectedness of modules. The probability of a module containing well-defined substructures is highest when the number of internal links is of the order of √(2L) or smaller. This resolution limit means that modules smaller than this scale may not be resolved, even if they are complete graphs connected by single bridges. The resolution limit depends on the degree of interconnectedness between communities and can reach values of the order of the size of the whole network. This implies that it is impossible to determine whether a module obtained through modularity optimization is a single module or a cluster of smaller modules. The paper also discusses the practical consequences of this result by analyzing partitions obtained through modularity optimization in artificial and real networks. The authors conclude that modularity optimization may miss important substructures in a network, and it is crucial to check the structure of all detected modules. The resolution limit of modularity is an intrinsic scale that depends on the number of links and the interconnectedness of modules, and it calls for a new theoretical framework that focuses on a local definition of community, regardless of its size.The paper discusses the resolution limit in community detection using modularity optimization. Modularity is a measure used to evaluate the quality of a network partition into communities. The authors show that modularity optimization may fail to identify small modules, even if they are clearly defined, due to an intrinsic scale dependent on the total number of links (L) and the interconnectedness of modules. The probability of a module containing well-defined substructures is highest when the number of internal links is of the order of √(2L) or smaller. This resolution limit means that modules smaller than this scale may not be resolved, even if they are complete graphs connected by single bridges. The resolution limit depends on the degree of interconnectedness between communities and can reach values of the order of the size of the whole network. This implies that it is impossible to determine whether a module obtained through modularity optimization is a single module or a cluster of smaller modules. The paper also discusses the practical consequences of this result by analyzing partitions obtained through modularity optimization in artificial and real networks. The authors conclude that modularity optimization may miss important substructures in a network, and it is crucial to check the structure of all detected modules. The resolution limit of modularity is an intrinsic scale that depends on the number of links and the interconnectedness of modules, and it calls for a new theoretical framework that focuses on a local definition of community, regardless of its size.