Open AccessComplex Network Community Extraction Based on Gaussian Mixture Model Algorithm
Author(s) -
Dai Ting-ting,
Dong Yan-shou,
Shan Chang-ji
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/267/4/042163
Subject(s) - principal component analysis , adjacency matrix , computer science , dimension (graph theory) , algorithm , gaussian , maximization , mixture model , gaussian network model , component (thermodynamics) , expectation–maximization algorithm , network model , complex network , adjacency list , division (mathematics) , mathematical optimization , data mining , mathematics , artificial intelligence , theoretical computer science , maximum likelihood , statistics , graph , physics , arithmetic , quantum mechanics , world wide web , pure mathematics , thermodynamics
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.