The paper by Fortunato and Barthélemy discusses the resolution limit in community detection using modularity optimization. Modularity is a measure used to quantify the quality of a partition of a network into communities, where a community is a subgraph with more internal links than expected by chance. The authors find that modularity optimization may fail to identify modules smaller than a scale dependent on the total number of links in the network and the degree of interconnectedness between modules. Specifically, modules with fewer than \(\sqrt{2L}\) internal links may not be resolved, even if they are complete graphs connected by single bridges. This resolution limit depends on the degree of interconnectedness between communities and can reach the size of the entire network. The paper also analyzes the consequences of this resolution limit by examining partitions obtained through modularity optimization in both artificial and real networks. The results highlight the need for further analysis of detected modules to verify their true nature, as modules identified through modularity optimization may actually be combinations of smaller modules. The authors conclude that modularity optimization might miss important substructures and suggest a new theoretical framework focusing on a local definition of community.The paper by Fortunato and Barthélemy discusses the resolution limit in community detection using modularity optimization. Modularity is a measure used to quantify the quality of a partition of a network into communities, where a community is a subgraph with more internal links than expected by chance. The authors find that modularity optimization may fail to identify modules smaller than a scale dependent on the total number of links in the network and the degree of interconnectedness between modules. Specifically, modules with fewer than \(\sqrt{2L}\) internal links may not be resolved, even if they are complete graphs connected by single bridges. This resolution limit depends on the degree of interconnectedness between communities and can reach the size of the entire network. The paper also analyzes the consequences of this resolution limit by examining partitions obtained through modularity optimization in both artificial and real networks. The results highlight the need for further analysis of detected modules to verify their true nature, as modules identified through modularity optimization may actually be combinations of smaller modules. The authors conclude that modularity optimization might miss important substructures and suggest a new theoretical framework focusing on a local definition of community.