DeepAI
Log In Sign Up

Efficient Estimation for Random Dot Product Graphs via a One-step Procedure

10/10/2019
by   Fangzheng Xie, et al.
0

We propose a one-step procedure to efficiently estimate the latent positions in random dot product graphs. Unlike the classical spectral-based methods such as the adjacency and Laplacian spectral embedding, the proposed one-step procedure takes both the low-rank structure of the expected value of the adjacency matrix and the Bernoulli likelihood information of the sampling model into account simultaneously. We show that for each individual vertex, the corresponding row of the one-step estimator converges to a multivariate normal distribution after proper scaling and centering up to an orthogonal transformation, with an efficient covariance matrix, provided that the initial estimator satisfies the so-called approximate linearization property. The one-step estimator improves the commonly-adopted spectral embedding methods in the following sense: Globally for all vertices, it yields a smaller asymptotic sum of squared-error, and locally for each individual vertex, the asymptotic covariance matrix of the corresponding row of the one-step estimator is smaller than those of the spectral embedding in spectra. The usefulness of the proposed one-step procedure is demonstrated via numerical examples and the analysis of a real-world Wikipedia graph dataset.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/28/2016

Limit theorems for eigenvectors of the normalized Laplacian for random graphs

We prove a central limit theorem for the components of the eigenvectors ...
04/26/2019

Optimal Bayesian Estimation for Random Dot Product Graphs

We propose a Bayesian approach, called the posterior spectral embedding,...
12/08/2021

Consistency of Spectral Seriation

Consider a random graph G of size N constructed according to a graphon w...
07/29/2012

Universally Consistent Latent Position Estimation and Vertex Classification for Random Dot Product Graphs

In this work we show that, using the eigen-decomposition of the adjacenc...
09/27/2021

Graph Encoder Embedding

In this paper we propose a lightning fast graph embedding method called ...
07/11/2018

Robust relative error estimation

Relative error estimation has been recently used in regression analysis....
07/04/2022

Statistical inference of random graphs with a surrogate likelihood function

Spectral estimators have been broadly applied to statistical network ana...