Covariate-assisted Spectral Clustering in Dynamic Networks

02/11/2018
by   Yubo Tao, et al.
0

In this paper, we study the community detection problem in the dynamic stochastic blockmodel and dynamic stochastic co-blockmodel with node covariates. Covariate-assisted spectral clustering methods for estimating group membership in dynamic directed and undirected graphs are developed respectively. Weak consistency property is proved and degree correction is studied as well. Simulation shows that our new method outperforms existing spectral clustering methods for studying dynamic networks.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/08/2014

Covariate-assisted spectral clustering

Biological and social systems consist of myriad interacting units. The i...
research
07/30/2022

Covariate-Assisted Community Detection on Sparse Networks

Community detection is an important problem when processing network data...
research
11/23/2020

Estimating network memberships by mixed regularized spectral clustering

Mixed membership community detection is a challenge problem in network a...
research
07/30/2021

Impact of regularization on spectral clustering under the mixed membership stochastic block model

Mixed membership community detection is a challenge problem in network a...
research
05/02/2017

Spectral clustering in the dynamic stochastic block model

In the present paper, we studied a Dynamic Stochastic Block Model (DSBM)...
research
06/27/2023

Network-Adjusted Covariates for Community Detection

Community detection is a crucial task in network analysis that can be si...
research
10/07/2017

A New Spectral Clustering Algorithm

We present a new clustering algorithm that is based on searching for nat...

Please sign up or login with your details

Forgot password? Click here to reset