Research on a large-scale community detection algorithm based on non-weighted graph

Community detection and partitioning has become a critical issue in large-scale social networks. However, the applicability of most existing methods is limited by computational costs. In order to improve the quality of community division and calculation efficiency, a community detection algorithm based on un-weighted graphs is proposed. This algorithm uses two parameters to measure the community to achieve community discovery, which is clustering coefficient and common neighbor similarity, and its effectiveness is proved by the academic formula. Experimental analysis was carried out using a real social network dataset, and compared with other algorithms proposed two methods. The experimental results show that the proposed method is more efficient and the computation time is linear. It is suitable for community detection in large-scale social networks.