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Computing communities in complex networks using the Dirichlet processing Gaussian mixture model with spectral clustering
Journal article   Peer reviewed

Computing communities in complex networks using the Dirichlet processing Gaussian mixture model with spectral clustering

Fang Hu, Yanhui Zhu, Jia Liu and Yalin Jia
Physics letters. A, Vol.383(9), pp.813-824
02/28/2019
DOI: https://doi.org/10.1016/j.physleta.2018.12.005
Web of Science ID: WOS:000460811200004

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Abstract

Community detection Complex networks Dirichlet process Gaussian mixture model Spectral clustering Algorithms Mathematical Models
Community detection becomes a significant tool for the complex network analysis. The study of the community detection algorithms has received an enormous amount of attention. It is still an open question whether a highly accurate and efficient algorithm is found in most data sets. We propose the Dirichlet Processing Gaussian Mixture Model with Spectral Clustering algorithm for detecting the community structures. The combination of traditional spectral algorithm and new non-parametric Bayesian model provides high accuracy and quality. We compare the proposed algorithm with other existing community detecting algorithms using different real-world data sets and computer-generated synthetic data sets. We show that the proposed algorithm results in high modularity, and better accuracy in a wide range of networks. We find that the proposed algorithm works best for the large size of the data sets.

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