ECV.Rank: estimates optimal low rank model for a network

In randnet: Random Network Model Estimation, Selection and Parameter Tuning

estimates optimal low rank model for a network

Description

estimates the optimal low rank model for a network

Usage

ECV.Rank(A, max.K, B = 3, holdout.p = 0.1, weighted = TRUE,mode="directed")

Arguments

A	adjacency matrix
max.K	maximum possible rank to check
B	number of replications in ECV
holdout.p	test set proportion
weighted	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.

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.

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

Author(s)

Tianxi Li, Elizaveta Levina, Ji Zhu
Maintainer: Tianxi Li [e-mail]

References

T. Li, E. Levina, and J. Zhu. Network cross-validation by edge sampling. Biometrika, 107(2), pp.257-276, 2020.

See Also

ECV.block

Examples

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

randnet documentation built on May 31, 2023, 6:44 p.m.