  Community structure identification has been an important research topic in
complex networks and there has been many algorithms proposed so far to detect
community structures in complex networks, where most of the algorithms are not
suitable for very large networks because of their time-complexity. Genetic
algorithm for detecting communities in complex networks, which is based on
optimizing network modularity using genetic algorithm, is presented here. It is
scalable to very large networks and does not need any priori knowledge about
number of communities or any threshold value. It has O(e) time-complexity where
e is the number of edges in the network. Its accuracy is tested with the known
Zachary Karate Club and College Football datasets. Enron e-mail dataset is used
for scalability test.
