Fast parallel community detection algorithm based on modularity

In recent years, detecting dense sub-graphs that are known as communities in massive graphs has been a common issue in different fields of science. It provides the facility of studying complex graphs by simplifying them through utilizing communities. Due to ceaseless increases in graph size that are used in social networks (with billions of nodes and edges), algorithm execution time is an important factor for detecting communities. To cope with this problem, a new parallel community detection algorithm is presented in this paper. The main idea behind the proposed method is to assign parallel threads for the calculation of adding qualified neighbor nodes to the community. Proposed algorithm is tested using a general PC (IntelCorei7, 4 GByte). It leads to abating the algorithm execution time from 25% to 78% compared to the fastest previous parallel algorithms.