clusters nodes by concensus (majority voting) initialized by regularized spectral clustering
community detection by concensus (majority voting) initialized by regularized spectral clustering
ConsensusClust(A,K,tau=0.25,lap=TRUE)
A |
adjacency matrix |
K |
number of communities |
tau |
reguarlization parameter for regularized spectral clustering. Default value is 0.25. Typically set between 0 and 1. If tau=0, no regularization is applied. |
lap |
indicator. If TRUE, the Laplacian matrix for initializing clustering. If FALSE, the adjacency matrix will be used. |
Community detection algorithm by majority voting algorithm of Gao et. al. (2016). When initialized by regularized spectral clustering, it is shown that the clustering accuracy of this algorithm gives minimax rate under the SBM. However, it can slow compared with spectral clustering.
cluster labels
Tianxi Li, Elizaveta Levina, Ji Zhu
Maintainer: Tianxi Li <tianxili@umich.edu>
Gao, C.; Ma, Z.; Zhang, A. Y. & Zhou, H. H. Achieving optimal misclassification proportion in stochastic block models The Journal of Machine Learning Research, JMLR. org, 2017, 18, 1980-2024
dt <- BlockModel.Gen(15,300,K=3,beta=0.2,rho=0) A <- dt$A #cc <- ConsensusClust(A,K=3,lap=TRUE) # takes about 25 seconds #NMI(cc,dt$g)
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