The community detection of hybrid node analysis and edge analysis in complex networks (CDHNE), a novel community detection algorithm, was proposed aiming at the problem that both edge community detection and node-based community detection algorithms had corresponding shortcomings in the process of detecting communities, which affected the quality of complex network community detection.The relatively stable characteristics of the edge in the networks were firstly used by the algorithm to construct a more accurate community structure through edge community detection at the early stage of algorithm execution.Then, after the formation of the edge communities, the flexible characteristics of the node were used to accurately detect the boundary of edge communities, so as to more accurately detect the community structure in the complex networks.In the computer-generated network experiments, when the community structure of the network gradually became fuzzy, the number of overlapping nodes and the number of communities to which the overlapping nodes belonged kept increasing.Compared to traditional algorithms, the accuracy of community detection and overlapping nodes detection were improved by an average of 10% and 15%, respectively, by the CDHNE algorithm.In the real network experiments, the tightness of the community structure detected by the CDHNE algorithm was better.Especially when facing large-scale networks with more than 100 000 nodes, the detection task was completed by the CDHNE algorithm with high quality, and the EQ value reached 0.412 1.The experimental results show that the CDHNE algorithm has advantages in operational stability and handling large-scale networks.
