LSM.PGD: estimates inner product latent space model by projected...

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

estimates inner product latent space model by projected gradient descent

Description

estimates inner product latent space model by projected gradient descent from the paper of Ma et al. (2020).

Usage

LSM.PGD(A, k,step.size=0.3,niter=500,trace=0)

Arguments

A	adjacency matrix
k	the dimension of the latent position
step.size	step size of gradient descent
niter	maximum number of iterations
trace	if trace > 0, the objective will be printed out after each iteration

Details

The method is based on the gradient descent of Ma et al (2020), with initialization of the universal singular value thresholding as discussed there. The parameter identifiability constraint is the same as in the paper.

Value

a list of

Z	latent positions
alpha	individual parameter alpha as in the paper
Phat	esitmated probability matrix
obj	the objective of the gradient method

Author(s)

Tianxi Li and Can M. Le
Maintainer: Tianxi Li [e-mail]

References

Z. Ma, Z. Ma, and H. Yuan. Universal latent space model fitting for large networks with edge covariates. Journal of Machine Learning Research, 21(4):1-67, 2020.

See Also

DCSBM.estimate

Examples

dt <- RDPG.Gen(n=600,K=2,directed=TRUE)


A <- dt$A


fit <- LSM.PGD(A,2,niter=50)

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