network.mixing: estimates network connection probability by network mixing

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

estimates network connection probability by network mixing

Description

estimates network connection probability by network mixing of Li and Le (2021).

Usage

network.mixing(A,index=NULL,rho = 0.1,max.K=15,dcsbm=TRUE, usvt=TRUE,ns=FALSE,
                           lsm=FALSE,lsm.k=4,trace=FALSE)

Arguments

A	adjacency matrix
index	a pre-specified hold-out set. If NULL, the set will be randomly generated according to rho.
rho	hold-out proportion as validation entries. Only effective when index is NULL.
max.K	the maximum number of blocks used for the block model approximation (see details).
dcsbm	whether to include the DCSBM as components, up to max.K. By default, the method will include it.
usvt	whether to include the USVT as a component. By default, the method will include it.
ns	whether to include the neighborhood smoothing as a component.
lsm	whether to include the gradient estimator of the latent space model as a component.
lsm.k	the dimension of the latent space. Only effective if lsm is TRUE.
trace	whether to print the model fitting progress.

Details

The basic version of the mixing estimator will include SBM and DCSBM estimates with the number of blocks from 1 to max.K. Users could also specify whether to include additional USVT, neighborhood smoothing and latent space model estimators. If NNL (non-negative linear), exponential, or ECV is used, the mixing is usually robust for a reasonable range of max.K and whether to include the other models. The linear mixing, however, is vulnerable for a large number of base estimates. The NNL is our recommended method. USVT is also recommended. the neighborhood smoothing and latent space model are slower, so are not suitable for large networks. Details can be found in Li and Le (2021).

Value

a list of

linear.Phat	estimated probability matrix by linear mixing
linear.weight	the weights of the indivdiual models in linear mixing
nnl.Phat	estimated probability matrix by NNL mixing
nnl.weight	the weights of the indivdiual models in NNL mixing
exp.Phat	estimated probability matrix by exponential mixing
exp.weight	the weights of the indivdiual models in exponential mixing
ecv.Phat	estimated probability matrix by ECV mixing (only one nonzero)
ecv.weight	the weights of the indivdiual models in ECV mixing (only one nonzero)
model.names	the names of all individual models, in the same order as the weights

Author(s)

Tianxi Li and Can M. Le

Maintainer: Tianxi Li <[e-mail]>

References

T. Li and C. M. Le, Network Estimation by Mixing: Adaptivity and More. arXiv preprint arXiv:2106.02803, 2021.

Examples

dt <- RDPG.Gen(n=500,K=5,directed=TRUE)

A <- dt$A

fit <- network.mixing(A)
fit$model.names

fit$nnl.weight

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