Efficient procedures for community detection in network studies, especially for sparse networks. The algorithms impose penalties on the differences of the coordinates which represent the community labels of the nodes.
|Author||Yang Feng, Richard J. Samworth and Yi Yu|
|Date of publication||2013-11-10 17:22:49|
|Maintainer||Yi Yu <firstname.lastname@example.org>|
|License||GPL (>= 2.0)|
fpca: Fused Principal Component Analysis path.
fpca.cluster: Clustering the estimators along the path.
fpca.cut: The ratio cut and normalised cut values along the path
fpca.mod: The modularity values based on the DCBM and SBM assumptions...
fused.trans: The graph based penalty transformation matrix
generate: generate adjacency matrix of stochastic blockmodel,...
get.cluster: Final estimators of the community labels
isolate: Isolated nodes collection
laplacian: Laplacian matrix
single.cut: Ratio cut and normalised cut values
single.mod: Modularity based on DCBM and SBM assumptions