Complex networks constitute the backbone of complex systems. They represent a powerful interpretation tool for describing and analyzing many different kinds of systems from biology, economics, engineering and social networks. Uncovering the community structure exhibited by real networks is a crucial step towards a better understanding of complex systems, revealing the internal organization of nodes. However, existing algorithms in the literature up-to-date present several crucial issues, and the question of how good an algorithm is, with respect to others, is still open. Recently, Newman  suggested modularity as a natural measure of the goodness of network community decompositions. Here we propose an implementation of an Immunological Algorithm, a population based computational systems inspired by the immune system and its features, to perform community detection on the methods of modularity maximization. The reliability and efficiency of the proposed algorithm has been validating by comparing it with Louvain algorithm one of the fastest and the popular algorithm based on a multiscale modularity optimization scheme.
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