Abstract
Based on the problem of community partitioning in complex networks,this paper proposes a Gaussian mixture model community extraction algorithm based on principal component analysis.The idea of the algorithm is as follows:Firstly,the principal component analysis is used to reduce the dimension of the adjacency matrix of the network;secondly,it is assumed that the communities in a network are generated by different Gaussian models,that is,the generation mechanism of different models is different;The parameters of the model are solved by the expectation maximization algorithm. Simulation experiments show that if the contribution rate of the principal component reaches more than 90%, the network division is very consistent with the actual network,and the time used is also short. Compared with other methods,it has obvious advantages.
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