Abstract:The community detection process is one of the major challenges in the era of big data analytics, especially in the area of socially complex networks. In order to improve the accuracy and efficiency of community detection, a social complex network community detection algorithm based on undirected graph and clustering is proposed. Two new metrics were first used to achieve community detection, namely clustering coefficients and common neighbor similarities. Then the complexity of the conceptual community detection is reduced to x based on efficient modularity, and the edges and nodes in the undirected graph are updated by balancing the binary tree, thereby reducing the computational workload. The experimental results show that the proposed algorithm has higher operating efficiency and accuracy than those of the other two algorithms.