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	Community detection in sample networks generated from Gaussian mixture model
	Zhao, Ling ; Liu, Tingzhan ; Liu, Jian
	2011
英文摘要	Detecting communities in complex networks is of great importance in sociology, biology and computer science, disciplines where systems are often represented as networks. In this paper, we use the coarse-grained-diffusion- distance based agglomerative algorithm to uncover the community structure exhibited by sample networks generated from Gaussian mixture model, in which the connectivity of the network is induced by a metric. The present algorithm can identify the community structure in a high degree of efficiency and accuracy. An appropriate number of communities can be automatically determined without any prior knowledge about the community structure. The computational results on three artificial networks confirm the capability of the algorithm. ? 2011 Springer-Verlag.; EI; 0
语种	英语
出处	EI
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/407879]
专题	数学科学学院
推荐引用方式 GB/T 7714	Zhao, Ling,Liu, Tingzhan,Liu, Jian. Community detection in sample networks generated from Gaussian mixture model. 2011-01-01.

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