Specific dimension reduction methods for replicated graphs
(multiple undirected graphs repeatedly measured on a common set of
nodes). The package contains efficient procedures for estimating
a shared baseline propensity matrix and graph-specific low rank
matrices. The algorithm uses block coordinate descent algorithm to
solve the model, which alternatively performs L2-penalized logistic
regression and multiple partial eigenvalue decompositions, as described
in the paper Wang et al. (2017)
CISE is an R package that fits the multiple-gragh factorization (M-GRAF) model ( Wang et al., 2017) for separating common and individual low rank structure for multiple graphs with a common set of nodes.
install.packages("CISE") # Or the development version from GitHub: # install.packages("devtools") devtools::install_github("wangronglu/CISE")
The package contains a dataset of binary brain networks of 212 subjects. After loading the data, we can apply 3 variants of M-GRAF model to decompose the data. Model checking is needed to evaluate which model provides the best fit.
library(CISE) # load data # A is an array of adjacency matrices of multiple graphs data(A) ? MGRAF2 # specify low rank K=5 res = MGRAF2(A = A, K=5, tol=0.01, maxit=5) # check the common structure - baseline propensities of edges between pairwise nodes res$Z # check low rank component for each graph res$Q # check joint log-likelihood across iterations res$LL