creating a maximal clique graph to improve community detection in scoda and oslom algorithms

Community detection is one of the important topics regarding complex network study. there are many community detection algorithms such as streaming community detection algorithm (scoda) and order statistics local optimization method (oslom). however, the performance of these algorithms, in overlap communities and communities with ambiguous structure, is problematic. in community detection algorithms achieving accurate results is a challenge. in this paper, we’ve proposed a method based on finding maximal cliques and generating the corresponding graph in order to use as an input to scoda and oslom algorithms. synthetic non-overlap and overlap graphs and real graphs data are used in our experiments. f1score and nm1 score functions are utilized as our evaluation criteria. we have shown that the improved version of scoda demonstrated better results in comparison to the original scoda algorithm, and the improved version of oslom was also superior in performance when compared with the original oslom algorithm.