Research Article
Finding Community Structure in Complex Network Based on Latent Variables
@INPROCEEDINGS{10.1109/ChinaCom.2012.6417483, author={Lin Li and Songnian Lu and Shenghong Li and Zhengmin Xia}, title={Finding Community Structure in Complex Network Based on Latent Variables}, proceedings={7th International Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2012}, month={9}, keywords={community detection random variables network sampling}, doi={10.1109/ChinaCom.2012.6417483} }- Lin Li
Songnian Lu
Shenghong Li
Zhengmin Xia
Year: 2012
Finding Community Structure in Complex Network Based on Latent Variables
CHINACOM
IEEE
DOI: 10.1109/ChinaCom.2012.6417483
Abstract
A number of recent studies have focused on detect-ing community structure in complex network. We develop an algorithm to detect communities by treating nodes as random variables and deriving samples of these variables from adja-cency matrix which includes topology information of network. Using factor analysis theory, we represent communities as latent variables and extract the related matrix to uncover the relation-ship between network nodes and communities. The algorithm proposed in this paper uses the related matrix to improve the testing accuracy and we also notice that the algorithm overcomes the resolution limit possessed by other modularity-based methods in a kind of network topology structure. Experiments in real-world networks reveal that it detects significant and informative community divisions compared with other classic methods
