The paper by Danon et al. (2009) compares various methods for identifying community structures in complex networks, focusing on their sensitivity and computational cost. The authors revisit the modularity measure, a widely used method for evaluating the quality of community partitions, and introduce a more accurate representation of algorithm sensitivity based on information theory. They compare the performance of different methods on *ad hoc* networks with known community structures, finding that more accurate methods tend to be more computationally expensive. The paper suggests that both aspects should be considered when choosing a method for practical applications. The authors also propose a standard benchmark test for community detection methods and provide recommendations for selecting appropriate algorithms based on the size and characteristics of the network. The study highlights the trade-offs between accuracy and computational efficiency and emphasizes the need for further research to develop faster and more accurate methods.The paper by Danon et al. (2009) compares various methods for identifying community structures in complex networks, focusing on their sensitivity and computational cost. The authors revisit the modularity measure, a widely used method for evaluating the quality of community partitions, and introduce a more accurate representation of algorithm sensitivity based on information theory. They compare the performance of different methods on *ad hoc* networks with known community structures, finding that more accurate methods tend to be more computationally expensive. The paper suggests that both aspects should be considered when choosing a method for practical applications. The authors also propose a standard benchmark test for community detection methods and provide recommendations for selecting appropriate algorithms based on the size and characteristics of the network. The study highlights the trade-offs between accuracy and computational efficiency and emphasizes the need for further research to develop faster and more accurate methods.