Creating a Maximal Clique Graph to Improve Community Detection in SCoDA and OSLOM Algorithms

Community detection is one of the important topics in complex network study. There are many algorithms for community detection, some of which are based on finding maximal cliques in the network. In this paper, we improve Streaming Community Detection Algorithm (SCoDA) and Order Statistics Local Optimization Method (OSLOM). After finding maximal cliques and generating the corresponding graphs, the latter are used as input to SCoDA and OSLOM algorithms. Non-overlap and overlap synthetic graphs and real graphs data are used in our experiments.  As evaluation criteria F1score and NMI scores functions are utilized. It is shown that the improved version of SCoDA has better results in comparison to the original SCoDA algorithm, and the improved OSLOM algorithm has better performance in comparison with the original OSLOM algorithm.