Title | Community detection in sample networks generated from Gaussian mixture model |
Authors | Zhao, Ling Liu, Tingzhan Liu, Jian |
Affiliation | Beijing University of Posts and Telecommunications, Beijing 100876, China School of Sciences, Communication University of China, Beijing 100024, China LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, China |
Issue Date | 2011 |
Citation | 2nd International Conference on Swarm Intelligence, ICSI 2011.Chongqing, China,6729 LNCS(183-190). |
Abstract | 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. |
URI | http://hdl.handle.net/20.500.11897/407879 |
DOI | 10.1007/978-3-642-21524-7_22 |
Indexed | EI |
Appears in Collections: | 数学科学学院 数学及其应用教育部重点实验室 |