A hierarchical model for community identification in complex networks through modularity and genetic algorithm

In recent years, the identification of communities inside complex networks has garnered considerable interest, with numerous proposed methodologies emphasizing modularity. Identifying communities can be regarded as a modularity optimization challenge; yet, conventional methods frequently encounter difficulties in detecting smaller communities due to resolution constraints. In this paper, a new method for hierarchical community detection is proposed using genetic techniques and the modularity criterion. The presented approach includes two phases. In the first phase, local communities are identified using a genetic algorithm. In this phase, the complex network structure is hierarchically decomposed into a collection of smaller communities or local communities. Subsequently, in the second phase, the identification of the main communities of the network is carried out through the iterative merging of local communities using the modularity criterion. The objective of this phase is to perform the merging in such a way that the modularity of the resulting communities is maximized. The results of the implementation show that the accuracies of 98%, 81% and 80% are achieved in networks with dimensions of 32, 64 and 128 respectively, which indicates the superior performance of the presented approach in contrast to compared algorithms.