The paper proposes a novel method for community detection in complex networks using extremal optimization (EO) to maximize the modularity $Q$. The method outperforms existing algorithms in terms of accuracy and efficiency. The authors present results on both simulated and real networks, demonstrating that their approach can accurately identify community structures in large complex networks. The algorithm is based on a heuristic search that moves nodes with the lowest fitness from one partition to another, repeatedly until an optimal state is reached. The performance of the EO algorithm is compared with other methods, showing superior results in identifying communities even in networks with less clear internal structure. The method's stochastic nature allows for multiple runs to yield consistent community partitions, further validating its effectiveness.The paper proposes a novel method for community detection in complex networks using extremal optimization (EO) to maximize the modularity $Q$. The method outperforms existing algorithms in terms of accuracy and efficiency. The authors present results on both simulated and real networks, demonstrating that their approach can accurately identify community structures in large complex networks. The algorithm is based on a heuristic search that moves nodes with the lowest fitness from one partition to another, repeatedly until an optimal state is reached. The performance of the EO algorithm is compared with other methods, showing superior results in identifying communities even in networks with less clear internal structure. The method's stochastic nature allows for multiple runs to yield consistent community partitions, further validating its effectiveness.