Complex Network Community Extraction Based on Gaussian Mixture Model Algorithm
| Autor: | Dai Ting-ting, Shan Chang-ji, Dong Yan-shou |
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| Rok vydání: | 2019 |
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| Zdroj: | IOP Conference Series: Earth and Environmental Science. 267:042163 |
| ISSN: | 1755-1315 1755-1307 |
| DOI: | 10.1088/1755-1315/267/4/042163 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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