Perfect Clustering for Stochastic Blockmodel Graphs via Adjacency Spectral Embedding

10/02/2013
by   Vince Lyzinski, et al.
0

Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the subject of extensive research. In thispaper, we provide a short proof that the adjacency spectral embedding can be used to obtain perfect clustering for the stochastic blockmodel and the degree-corrected stochastic blockmodel. We also show an analogous result for the more general random dot product graph model.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/16/2019

Privacy Preserving Adjacency Spectral Embedding on Stochastic Blockmodels

For graphs generated from stochastic blockmodels, adjacency spectral emb...
research
05/03/2021

Spectral clustering under degree heterogeneity: a case for the random walk Laplacian

This paper shows that graph spectral embedding using the random walk Lap...
research
11/23/2013

Robust Vertex Classification

For random graphs distributed according to stochastic blockmodels, a spe...
research
08/23/2018

On a 'Two Truths' Phenomenon in Spectral Graph Clustering

Clustering is concerned with coherently grouping observations without an...
research
12/23/2019

Spectral embedding of regularized block models

Spectral embedding is a popular technique for the representation of grap...
research
06/02/2021

Spectral embedding for dynamic networks with stability guarantees

We consider the problem of embedding a dynamic network, to obtain time-e...
research
11/09/2017

Toward perfect reads

We propose a new method to correct short reads using de Bruijn graphs, a...

Please sign up or login with your details

Forgot password? Click here to reset