Complex Network Community Extraction Based on Gaussian Mixture Model Algorithm

2019 3rd International Workshop on Renewable Energy and Development

能源学;生态环境科学

Ting-Ting, Dai^1 ; Yan-Shou, Dong^2 ; Chang-Ji, Shan^2

School of Mathematics and Statistics, Zhaotong University, Yunnan

657000, China^1

School of Physics and Electronic Information Engineering, Zhaotong University, Yunnan

657000, China^2

关键词: Adjacency matrices;    Contribution rate;    Expectation-maximization algorithms;    Gaussian Mixture Model;    Generation mechanism;    Network communities;    Network divisions;    Principal Components;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/267/4/042163/pdf
DOI  :  10.1088/1755-1315/267/4/042163

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

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.