Computing communities in complex networks using the Dirichlet processing Gaussian mixture model with spectral clustering |
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Authors: | Fang Hu Yanhui Zhu Jia Liu Yalin Jia |
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Institution: | 1. College of Information Engineering, Hubei University of Chinese Medicine, Wuhan 430065, PR China;2. Department of Mathematics and Statistics, University of West Florida, Pensacola, 32514, USA |
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Abstract: | 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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Keywords: | Complex networks Community detection Spectral clustering Dirichlet process Gaussian mixture model |
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