whether the network is weighted. If TRUE, only sum of squared errors
are computed. If FALSE, then treat the network as
binary and AUC will be computed along with SSE.
mode
Selectign the mode of "directed" or "undirected" for cross-validation.
Value
A list of
sse.rank
rank selection by SSE loss
auc.rank
rank selection by AUC loss
auc
auc sequence for each rank candidate
sse
sse sequence for each rank candidate
%% ...
Details
AUC is believed to be more accurate in
many simulations for binary networks. But the computation of AUC is much
slower than SSE, even slower than matrix completion steps.
Note that we do not have to assume the true model is low rank. This
function simply finds a best low-rank approximation to the true model.
References
T. Li, E. Levina, and J. Zhu. Network cross-validation by edge sampling. Biometrika, 107(2), pp.257-276, 2020.
# NOT RUN {dt <- BlockModel.Gen(30,300,K=3,beta=0.2,rho=0.9,simple=FALSE,power=TRUE)
A <- dt$A
ecv.rank <- ECV.Rank(A,6,weighted=FALSE,mode="undirected")
ecv.rank
# }